5.3 Image Restoration, Image Denoising

Linfoot, E.H.,
Contrast Transmission Functions at Low Spatial Frequencies,
Optica Acta(6), No. 4, October 1959, pp. 387-404. BibRef 5910

Barnes, C.W.,
Object Restoration in a Diffraction Limited Imaging System,
JOSA(56), 1966, pp. 575-576. BibRef 6600

Müller, P.F., Reynolds, G.O.,
Image Restoration by Removal of Random-Media Degradations,
JOSA(57), No. 11, November 1967, pp. 1338-1345. BibRef 6711

Yu, F.T.S.,
Image Restoration, Uncertainty, and Information,
AppOpt(8), No. 1, January 1969, pp. 53. BibRef 6901

Boulter, J.F.,
Interactive Digital Image Restoration and Enhancement,
CGIP(11), No. 4, December 1979, pp. 301-312.
Elsevier DOI BibRef 7912

Frieden, B.R.,
Statistical Models for the Image Restoration Problem,
CGIP(12), No. 1, January 1980, pp. 40-59.
Elsevier DOI BibRef 8001

Jain, A.K., and Angel, E.,
Image Restoration, Modelling, and Reduction of Dimensionality,
TC(23), No. 5, May 1974, pp. 470-476. BibRef 7405

Sondhi, M.M.,
Image Restoration: The Removal of Spatially Invariant Degradations,
PIEEE(60), No. 7, July 1972, pp. 842-853. BibRef 7207

Anderson, C.L., and Netravali, A.N.,
Image Restoration Based on a Subjective Criterion,
SMC(6), December 1976, pp. 845-853. BibRef 7612

Ostrem, J.S., and Falconer, D.G.,
A Differential Operator Technique for Restoring Degraded Signals and Images,
PAMI(3), No. 3, May 1981, pp. 278-284. BibRef 8105

Stuller, J.A., McGillem, C.D.,
Correction of a Statement in Digital Picture Processing,
CVGIP(22), No. 3, June 1983, pp. 409.
Elsevier DOI BibRef 8306

Meinel, E.S.,
Origins of linear and nonlinear recursive restoration algorithms,
JOSA-A(3), 1986, pp. 787-799. BibRef 8600

Aravena, J.L., and Porter, W.A.,
One-Dimensional Scan Selection for Two-Dimensional Signal Restoration,
PAMI(8), No. 2, March 1986, pp. 282-284. BibRef 8603

Murray, D.W., Kashko, A., Buxton, H.,
A Parallel Approach to the Picture Restoration Algorithm of Geman and Geman on an SIMD Machine,
IVC(4), No. 3, August 1986, pp. 133-142.
Elsevier DOI
See also Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. BibRef 8608

Cai, Z.G.[Zu-Guang],
Restoration of Binary Images Using Contour Direction Chain Codes Description,
CVGIP(41), No. 1, January 1988, pp. 101-106.
Elsevier DOI BibRef 8801

Bumbaca, F.[Federico], Smith, K.C.[Kennety C.],
A Practical Approach to Image Restoration for Computer Vision,
CVGIP(42), No. 2, May 1988, pp. 220-233.
Elsevier DOI BibRef 8805

Gidas, B.,
A Renormalization Group Approach to Image Processing Problems,
PAMI(11), No. 2, February 1989, pp. 164-180.
IEEE DOI BibRef 8902

Wang, Y.M.[Yuan Mei], and Lu, W.X.[Wei Xue],
Multicriterion Image Reconstruction and Implementation,
CVGIP(46), No. 1, April 1989, pp. 131-135.
Elsevier DOI BibRef 8904

Cocozza-Thivent, C.[Christiane], Bekkhoucha, A.[Abdelkrim],
Estimation in Pickard Random Fields and Application to Image Processing,
PR(26), No. 5, May 1993, pp. 747-761.
Elsevier DOI 0401
BibRef

Bhatt, M.R., Desai, U.B.,
Robust Image Restoration Algorithm Using Markov Random Field Model,
GMIP(56), No. 1, January 1994, pp. 61-yy. BibRef 9401

Nanda, P.K., Desai, U.B., Poonacha, P.G.,
Joint parameter estimation and restoration using MRF models and homotopy continuation method,
ICIP94(II: 705-709).
IEEE DOI 9411
BibRef

Nanda, P.K., Kumar, K. .S.I.[K. Sun-Il], Ghokale, S., Desai, U.B.,
A multiresolution approach to color image restoration and parameter estimation using homotopy continuation method,
ICIP95(II: 45-48).
IEEE DOI 9510
BibRef

Terebizh, V.Y.,
Occamian Approach in the Image Restoration and Other Inverse Problems,
IJIST(6), No. 4, Winter 1995, pp. 358-369. BibRef 9500

Azencott, R., Chalmond, B., Wang, J.P.,
Transfer-Function Estimation, Film Fusion and Image Restoration,
GMIP(58), No. 1, January 1996, pp. 65-74. BibRef 9601

Zhang, B., Shirazi, M.N., Noda, H.,
Blind Restoration of Degraded Binary Markov Random-Field Images,
GMIP(58), No. 1, January 1996, pp. 90-98. BibRef 9601

Guan, L.,
An Optimal Neuron Evolution Algorithm for Constrained Quadratic-Programming in Image Restoration,
SMC-A(26), No. 4, July 1996, pp. 513-518.
IEEE Top Reference. 9607
BibRef

Taratorin, A.M., Sideman, S.,
Constrained Regularized Image Restoration Using Projection on a Set of Regularized Solutions,
TSP(44), No. 6, June 1996, pp. 1547-1548. 9607
BibRef

Sundaram, R., Ersoy, O.K.,
Partitioning of Hopfield Networks and Its Application to Image Restoration,
OptEng(35), No. 8, August 1996, pp. 2276-2285. 9609
BibRef

Snyder, D.L.[Donald L.],
Image Recovery from Correlations,
JOSA-A(9), No. 8, August 1992, pp. 1266-1272. 9702
BibRef

Snyder, D.L.[Donald L.], Hammoud, A., White, R.,
Image Recovery from Data Acquired with a Charge-Coupled Device Camera,
JOSA-A(10), No. 5, May 1993, pp. 1014-1023. BibRef 9305

Leahy, R.M., Goutis, C.E.,
An Optimal Technique for Constraint-Based Image Restoration and Reconstruction,
ASSP(34), 1986, pp. 1629-1642. BibRef 8600

Koufopavlou, O.G., Goutis, C.E.,
Image reconstruction on a special purpose array processor,
IVC(10), No. 7, September 1992, pp. 479-484.
Elsevier DOI 0401
BibRef

Sanz, J.L.C., Huang, T.S.,
Discrete and Continuous Band-Limited Signal Extrapolation,
ASSP(31), 1983, pp. 1276-1285. BibRef 8300

Geman, D., Yang, C.D.[Cheng-Da],
Nonlinear image recovery with half-quadratic regularization,
IP(4), No. 7, July 1995, pp. 932-946.
IEEE DOI 0402
BibRef

Hebert, T.J., Lu, K.M.[Ke-Ming],
Expectation-maximization algorithms, null spaces, and MAP image restoration,
IP(4), No. 8, August 1995, pp. 1084-1095.
IEEE DOI 0402
BibRef

Sayrol, E., Nikias, C.L., Gasull, A.,
Image restoration using the W-slice method,
IP(4), No. 8, August 1995, pp. 1174-1181.
IEEE DOI 0402
BibRef

Jones, P.F., Aitken, G.J.M.,
Restoration of Images of Partially Obscured Objects,
JOSA-A(14), No. 5, May 1997, pp. 1015-1023. 9705
BibRef

Yang, Y.Y.[Yong-Yi], Galatsanos, N.P.,
Removal of Compression Artifacts Using Projections onto Convex Sets and Line Process Modeling,
IP(6), No. 10, October 1997, pp. 1345-1357.
IEEE DOI 9710
BibRef
Earlier:
Edge-preserving reconstruction of compressed images using projections and a divide-and-conquer strategy,
ICIP94(II: 535-539).
IEEE DOI 9411
BibRef

Verveer, P.J., Jovin, T.M.,
Image-Restoration Based on Goods Roughness Penalty with Application to Fluorescence Microscopy,
JOSA-A(15), No. 5, May 1998, pp. 1077-1083. 9805
BibRef

Morita, T., Tanaka, K.,
Cluster ZTP in the Recovery of an Image,
PRL(18), No. 14, December 1997, pp. 1479-1493. 9806
BibRef

Mollah, M.M., Yahagi, T.,
Estimation of 2-D Noncausal AR Parameters for Image-Restoration Using Genetic Algorithm,
IEICE(E81-A), No. 8, August 1998, pp. 1676-1682. 9809
BibRef

Wober, M.A.[Munib A.], Yang, Y.B.[Yi-Bing], Hajjahmad, I.[Ibrahim], Sunshine, L.E.[Lon E.], Reisch, M.L.[Michael L.],
Image noise reduction system using a wiener variant filter in a pyramid image representation,
US_Patent5,729,631, Mar 17, 1998
WWW Link. BibRef 9803

Nikolova, M.[Mila],
Markovian Reconstruction Using a GNC Approach,
IP(8), No. 9, September 1999, pp. 1204-1220.
IEEE DOI BibRef 9909
Earlier:
Regularisation Functions and Estimators,
ICIP96(II: 457-460).
IEEE DOI BibRef

Nikolova, M.,
Image restoration by minimizing objective functions with non-smooth data-fidelity terms,
LevelSet01(xx-yy). 0106
BibRef
And:
Smoothing of Outliers in Image Restoration by Minimizing Regularized Objective Functions with Nonsmooth Data-fidelity Terms,
ICIP01(I: 233-236).
IEEE DOI 0108
BibRef

Nikolova, M.[Mila],
Weakly Constrained Minimization: Application to the Estimation of Images and Signals Involving Constant Regions,
JMIV(21), No. 2, September 2004, pp. 155-175.
DOI Link 0409
BibRef

Chan, T.F., Esedoglu, S., Nikolova, M.[Mila],
Finding the Global Minimum for Binary Image Restoration,
ICIP05(I: 121-124).
IEEE DOI 0512
BibRef

Nikolova, M.[Mila],
Counter-Examples for Bayesian MAP Restoration,
SSVM07(140-152).
Springer DOI 0705
BibRef

Nikolova, M.[Mila],
Semi-explicit Solution and Fast Minimization Scheme for an Energy with L1-Fitting and Tikhonov-Like Regularization,
JMIV(34), No. 1, May 2009, pp. xx-yy.
Springer DOI 0905

See also Regularization of Ill-Posed Problems, The. BibRef

Nikolova, M.[Mila],
Description of the Minimizers of Least Squares Regularized with L_0-norm. Uniqueness of the Global Minimizer,
SIIMS(6), No. 2, 2013, pp. 904-937.
DOI Link 1307
BibRef
Earlier:
Should We Search for a Global Minimizer of Least Squares Regularized with an L_0 Penalty to Get the Exact Solution of an under Determined Linear System?,
SSVM11(508-519).
Springer DOI 1201
BibRef
Earlier:
Bounds on the Minimizers of (nonconvex) Regularized Least-Squares,
SSVM07(496-507).
Springer DOI 0705
BibRef

Nikolova, M.[Mila],
Either Fit to Data Entries or Locally to Prior: The Minimizers of Objectives with Nonsmooth Nonconvex Data Fidelity and Regularization,
SSVM11(110-121).
Springer DOI 1201
BibRef

Durand, S., Nikolova, M.,
Stability of image restoration by minimizing regularized objective functions,
LevelSet01(xx-yy). 0106
BibRef

Nikolova, M.[Mila], Ng, M.K.[Michael K.], Zhang, S.Q.[Shu-Qin], Ching, W.K.[Wai-Ki],
Efficient Reconstruction Of Piecewise Constant Images Using Nonsmooth Nonconvex Minimization,
SIIMS(1), No. 1, 2008, pp. 2-25. image restoration; regularization; nonsmooth and nonconvex optimization; continuation; interior point method; unsupervised segmentation; inverse problems; deblurring; constrained optimization; graduated nonconvexity (GNC)
DOI Link BibRef 0800

Nikolova, M.[Mila], Ng, M.K.[Michael K.], Tam, C.P.,
Fast Nonconvex Nonsmooth Minimization Methods for Image Restoration and Reconstruction,
IP(19), No. 12, December 2010, pp. 3073-3088.
IEEE DOI 1011
BibRef

Voss, K.[Klaus], Suesse, H.[Herbert], Ortmann, W.[Wolfgang], Baumbach, T.[Torsten],
Shift Detection by Restoration,
PR(32), No. 12, December 1999, pp. 2067-2068.
Elsevier DOI BibRef 9912
Earlier: A2, A1, A3, A4: CAIP99(33-40).
Springer DOI 9909
BibRef

Baumbach, T., Ortmann, W.,
Shift detection by restoration: Demonstrated by signal based point pattern matching,
CIAP99(310-315).
IEEE DOI 9909
BibRef

Hunt, B.R.[Bobby R.],
Prospects for image restoration,
Modern Physics C(5), 1994, pp. 151-178. BibRef 9400

Miller, C.[Casey], Hunt, B.R.[Bobby R.], Marcellin, M.W.[Michael W.], Neifeld, M.A.[Mark A.],
Image restoration with the Viterbi algorithm,
JOSA-A(17), No. 2, February 2000, pp. 265-275. 0002
BibRef

Miller, C., Hunt, B.R., Neifeld, M.A., and Marcellin, M.W.,
Binary Image Reconstruction via 2-D Viterbi Search,
ICIP97(I: 181-184).
IEEE DOI BibRef 9700

Li, S.Z.[Stan Z.],
Toward global solution to MAP image restoration and segmentation: Using Common Structure of Local Minima,
PR(33), No. 4, April 2000, pp. 715-723.
Elsevier DOI 0002
BibRef

Storvik, G., Dahl, G.,
Lagrangian-Based Methods for Finding MAP Solutions for MRF Models,
IP(9), No. 3, March 2000, pp. 469-479.
IEEE DOI 0003
BibRef

van Kempen, G.M.P.[Geert M. P.], van Vliet, L.J.[Lucas J.],
Background estimation in nonlinear image restoration,
JOSA-A(17), No. 3, March 2000, pp. 425-433. 0003
BibRef

Sigelle, M.[Marc],
A Cumulant Expansion Technique for Simultaneous Markov Random Field Image Restoration and Hyperparameter Estimation,
IJCV(37), No. 3, June 2000, pp. 275-293.
DOI Link 0008
BibRef

Choy, S.O., Chan, Y.H., Siu, W.C.,
Image restoration by regularisation in uncorrelated transform domain,
VISP(147), No. 6, December 2000, pp. 587-594. 0101
BibRef

Meyer, Y.,
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations,
AMS2002. University Lecture Series, Vol. 22. Images decompose into geometry and texture. BibRef 0200

Meyer, Y.,
Oscillating Patterns in Some Nonlinear Evolution Equations,
SpringerLNM 1871, 2006, pp. 101-187.
Springer DOI BibRef 0600

Nagorni, M.[Matthias], Hell, S.W.[Stefan W.],
Coherent use of opposing lenses for axial resolution increase. II. Power and limitation of nonlinear image restoration,
JOSA-A(18), No. 1, January 2001, pp. 49-54. 0101
BibRef

Markham, J.[Joanne], Conchello, J.A.[Jose-Angel],
Fast maximum-likelihood image-restoration algorithms for three-dimensional fluorescence microscopy,
JOSA-A(18), No. 5, May 2001, pp. 1062-1071. 0105
BibRef

Piccolomini, E.L.[E. Loli], Zama, F.,
Parallel Image Restoration with Domain Decomposition,
RealTimeImg(7), No. 1, February 2001, pp. 47-57.
DOI Link 0106
BibRef

Idier, J.,
Convex half-quadratic criteria and interacting auxiliary variables for image restoration,
IP(10), No. 7, July 2001, pp. 1001-1009.
IEEE DOI 0108
BibRef

Chouzenoux, E., Idier, J., Moussaoui, S.,
A Majorize-Minimize Strategy for Subspace Optimization Applied to Image Restoration,
IP(20), No. 6, June 2011, pp. 1517-1528.
IEEE DOI 1106
accelerated subspace optimization. BibRef

Pham, T.D.[Tuan D.],
An image restoration by fusion,
PR(34), No. 12, December 2001, pp. 2403-2411.
Elsevier DOI 0110
BibRef

Pham, T.D.[Tuan D.],
Fuzzy posterior-probabilistic fusion,
PR(44), No. 5, May 2011, pp. 1023-1030.
Elsevier DOI 1101
Information fusion; Permanence of ratios; Probabilistic measures; Fuzzy measures; Fuzzy integrals
See also Fuzzy declustering-based vector quantization. BibRef

Pham, T.D.[Tuan D.],
Image Classification with Indicator Kriging Error Comparison,
ICISP14(433-440).
Springer DOI 1406
BibRef

Pham, T.D.,
Image Restoration by Ordinary Kriging with Convexity,
ICPR00(Vol III: 330-333).
IEEE DOI 0009
BibRef

Pham, T.D.[Tuan D.],
Double Adaptive Filtering of Gaussian Noise Degraded Images,
SCIA07(848-857).
Springer DOI 0706
BibRef

Pham, T.D., Wagner, M.,
Image Restoration by Fuzzy Convex Ordinary Kriging,
ICIP00(Vol I: 113-116).
IEEE DOI 0008
BibRef

Pham, T.D.[Tuan D.], Eisenblatter, U.[Uwe],
A New Spatial Approach to Image Restoration,
IPTA08(1-8).
IEEE DOI 0811
BibRef

Unal, G.B., Cetin, A.E.,
Restoration of error-diffused images using projection onto convex sets,
IP(10), No. 12, December 2001, pp. 1836-1841.
IEEE DOI 0201
BibRef

Sotthivirat, S., Fessler, J.A.,
Image Recovery Using Partitioned-Separable Paraboloidal Surrogate Coordinate Ascent Algorithms,
IP(11), No. 3, March 2002, pp. 306-317.
IEEE DOI 0203
BibRef
Earlier:
Partitioned Separable Paraboloidal Surrogate Coordinate Ascent Algorithm for Image Restoration,
ICIP00(Vol I: 109-112).
IEEE DOI 0008
BibRef

Zhang, D.D.[David D.], Wang, Z.[Zhou],
Image information restoration based on long-range correlation,
CirSysVideo(12), No. 5, May 2002, pp. 331-341.
IEEE Top Reference. 0206
BibRef

Sequeira, R.E., Gubner, J.A., Saleh, B.E.A.,
Image detection under low-level illumination,
IP(2), No. 1, January 1993, pp. 18-26.
IEEE DOI 0402
BibRef

Kim, J.B.[Jong Bae], Kim, H.J.[Hang Joon],
GA-Based Image Restoration by Isophote Constraint Optimization,
JASP(2003), No. 3, March 2003, pp. 238.
WWW Link. 0304
BibRef

Pham, D.S.[Due Son], Zoubir, A.M.,
A sequential algorithm for robust parameter estimation,
SPLetters(12), No. 1, January 2005, pp. 21-24.
IEEE Abstract. 0501
BibRef

Rabie, T.,
Robust Estimation Approach for Blind Denoising,
IP(14), No. 11, November 2005, pp. 1755-1765.
IEEE DOI 0510
BibRef

Liao, Y., Lin, X.,
Blind Image Restoration With Eigen-Face Subspace,
IP(14), No. 11, November 2005, pp. 1766-1772.
IEEE DOI 0510
BibRef

Nadadur, D., Haralick, R.M., Gustafson, D.E.,
A Bayesian Framework for Noise Covariance Estimation Using the Facet Model,
IP(14), No. 11, November 2005, pp. 1902-1917.
IEEE DOI 0510
BibRef

Memon, N., Pal, A.,
Automated Reassembly of File Fragmented Images Using Greedy Algorithms,
IP(15), No. 2, February 2006, pp. 385-393.
IEEE DOI 0602
Pose as a graph problem, find the best ordering. BibRef

Zhu, D.[Daan], Razaz, M.[Moe], Fisher, M.[Mark],
An adaptive algorithm for image restoration using combined penalty functions,
PRL(27), No. 12, September 2006, pp. 1336-1341.
Elsevier DOI 0606
Gradient descent; Penalized likelihood; Combined penalty function; Regularization BibRef

Zhu, D.[Daan], Razaz, M., Lee, R.,
A robust regularised restoration algorithm based on Topkis-Veinott optimisation method,
ICPR04(IV: 705-708).
IEEE DOI 0409
BibRef
And:
A Landweber Algorithm for 3D Confocal Microscopy Restoration,
ICPR04(I: 552-555).
IEEE DOI 0409
BibRef

Esmer, G.B.[G. Bora], Uzunov, V.[Vladislav], Onural, L.[Levent], Ozaktas, H.M.[Haldun M.], Gotchev, A.[Atanas],
Diffraction field computation from arbitrarily distributed data points in space,
SP:IC(22), No. 2, February 2007, pp. 178-187.
Elsevier DOI 0704
Scalar optical diffraction; Plane wave decomposition; Pseudo-matrix inversion; Projection onto convex sets BibRef

Esmer, G.B.[G. Bora], Onural, L.[Levent], Ozaktas, H.M.[Haldun M.], Uzunov, V.[Vladislav], Gotchev, A.[Atanas],
Performance Assessment of A Diffraction Field Computation Method Based on Source Model,
3DTV08(257-260).
IEEE DOI 0805
BibRef

Esmer, G.B.[G. Bora], Uzunov, V.[Vladislav], Onural, L.[Levent], Gotchev, A.[Atanas], Ozaktas, H.M.[Haldun M.],
Reconstruction of Scalar Diffraction Field from Distributed Data Points Over 3D Space,
3DTV07(1-4).
IEEE DOI 0705
BibRef

Uzunov, V.[Vladislav], Esmer, G.B.[G. Bora], Gotchev, A.[Atanas], Onural, L.[Levent], Ozaktas, H.M.[Haldun M.],
Bessel Functions-Based Reconstruction of Non-Uniformly Sampled Diffraction Fields,
3DTV07(1-4).
IEEE DOI 0705
BibRef

Oommen, B.J.[B. John], Kim, S.W.[Sang-Woon], Horn, G.[Geir],
On the estimation of independent binomial random variables using occurrence and sequential information,
PR(40), No. 11, November 2007, pp. 3263-3276.
Elsevier DOI 0707
BibRef
Earlier:
On the Theory and Applications of Sequence Based Estimation of Independent Binomial Random Variables,
SSPR06(8-21).
Springer DOI 0608
Estimation using sequential information; Estimation of binomials; Fused estimation methods; Sequential information BibRef

Oommen, B.J.[B. John], Kim, S.W.[Sang-Woon],
Multinomial Sequence Based Estimation Using Contiguous Subsequences of Length Three,
ICIAR16(243-253).
Springer DOI 1608
BibRef

Pan, P., Schonfeld, D.,
Image Reconstruction and Multidimensional Field Estimation From Randomly Scattered Sensors,
IP(17), No. 1, January 2008, pp. 94-99.
IEEE DOI 0712
Image reconstruction from photon-limited images and field estimation from scattered sensors. BibRef

Sha, L., Schonfeld, D., Wang, J.,
Graph Laplacian Regularization With Sparse Coding for Image Restoration and Representation,
CirSysVideo(30), No. 7, July 2020, pp. 2000-2014.
IEEE DOI 2007
Image restoration, Image coding, Laplace equations, Clustering algorithms, Image edge detection, Encoding, image decomposition BibRef

Chaudhry, A.[Asmatullah], Khan, A.[Asifullah], Ali, A.[Asad], Mirza, A.M.[Anwar M.],
A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming,
IJIST(17), No. 4, 2007, pp. 224-231.
DOI Link 0712
BibRef

Hussain, A.[Ayyaz], Jaffar, M.A.[M. Arfan], Siddiqui, A.B.[Abdul Basit], Nazir, M.[Muhammad], Mirza, A.M.[Anwar M.],
Modified Histogram Based Fuzzy Filter,
MIRAGE09(277-284).
Springer DOI 0905
BibRef

Gan, T., He, Y., Zhu, W.,
Fast M-Term Pursuit for Sparse Image Representation,
SPLetters(15), No. 1, January 2008, pp. 116-119.
IEEE DOI 0801
Extension of matching pursuit, but much faster. BibRef

Bao, P.[Paul], Hong, S.W.[Sung-Wai],
Image Restoration Based on Generalized Finite Automata Encoded Edge Preserving Regularization,
IJIG(2), No. 3, July 2002, pp. 425-439. 0207
BibRef

Goldfarb, D.[Donald], Wen, Z.W.[Zai-Wen], Yin, W.T.[Wo-Tao],
A Curvilinear Search Method For P-Harmonic Flows On Spheres,
SIIMS(2), No. 1, 2009, pp. 84-109.
DOI Link energy minimization; p-harmonic maps; p-harmonic flows; finite difference; curvilinear search; global convergence; chromaticity denoising BibRef 0900

Guidara, R., Hosseini, S., Deville, Y.,
Maximum Likelihood Blind Image Separation Using Nonsymmetrical Half-Plane Markov Random Fields,
IP(18), No. 11, November 2009, pp. 2435-2450.
IEEE DOI 0911
Separating mixtures of images. BibRef

Barbu, A.[Adrian],
Training an Active Random Field for Real-Time Image Denoising,
IP(18), No. 11, November 2009, pp. 2451-2462.
IEEE DOI 0911
BibRef
And:
Learning real-time MRF inference for image denoising,
CVPR09(1574-1581).
IEEE DOI 0906
Train MRF to perform well with suboptimal inference (i.e. not full maximum a posteriori estimation). BibRef

Luo, J., Li, W., Zhu, Y.,
Reconstruction From Limited-Angle Projections Based on delta-u Spectrum Analysis,
IP(19), No. 1, January 2010, pp. 131-140.
IEEE DOI 1001
Product of Delta function and step function for representation. BibRef

Wang, Z., Arce, G.R.[Gonzalo R.],
Variable Density Compressed Image Sampling,
IP(19), No. 1, January 2010, pp. 264-270.
IEEE DOI 1001
Reconstruction from limited number of samples at sub-Nyquist sampling rates. BibRef

Qiao, W.[Wei], Liu, B.[Bin], Xiong, Z.X.[Zi-Xiang], Arce, G.R.[Gonzalo R.], Garcia-Frias, J.[Javier], Zhu, W.[Wenwu], Yan, Z.S.[Zhi-Sheng],
Block-based variable density compressed image sampling,
ICIP12(909-912).
IEEE DOI 1302
BibRef

Wang, Z.[Zhe], Luo, S.W.[Si-Wei], Wang, L.[Liang],
A Fast Algorithm for Learning the Overcomplete Image Prior,
IEICE(E93-D), No. 2, February 2010, pp. 403-406.
WWW Link. 1002
BibRef

Wang, Z.[Zhe], Huang, Y.P.[Ya-Ping], Luo, S.W.[Si-Wei], Wang, L.[Liang],
Complex Cell Descriptor Learning for Robust Object Recognition,
IEICE(E94-D), No. 7, July 2011, pp. 1502-1505.
WWW Link. 1107
BibRef

Zhang, L.[Lei], Dong, W.S.[Wei-Sheng], Wu, X.L.[Xiao-Lin], Shi, G.M.[Guang-Ming],
Spatial-Temporal Color Video Reconstruction From Noisy CFA Sequence,
CirSysVideo(20), No. 6, June 2010, pp. 838-847.
IEEE DOI 1007
Single sensor color, CFA for video, demosaicing for color. Reduce sensor noise. BibRef

Papa, J.P.[Joao P.], Fonseca, L.M.G.[Leila M.G.], de Carvalho, L.A.S.[Lino A.S.],
Projections Onto Convex Sets through Particle Swarm Optimization and its application for remote sensing image restoration,
PRL(31), No. 13, 1 October 2010, pp. 1876-1886.
Elsevier DOI 1003
Image restoration; Projections Onto Convex Sets; Particle Swarm Optimization; CBERS-2B BibRef

de Souza, G.B.[Gustavo Botelho], da Silva Santos, D.F.[Daniel Felipe], Gonsalves Pires, R.[Rafael], Marana, A.N.[Aparecido Nilceu], Papa, J.P.[João Paulo],
A Restricted Boltzmann Machine-Based Approach for Robust Dimensionality Reduction,
WVC17(138-143)
IEEE DOI 1804
Boltzmann machines, face recognition, feature extraction, learning (artificial intelligence), optimisation, Restricted Boltzmann Machines BibRef

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A Deep Boltzmann Machine-Based Approach for Robust Image Denoising,
CIARP17(525-533).
Springer DOI 1802
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Karci, M.H., Demirekler, M.,
Minimization of Monotonically Levelable Higher Order MRF Energies via Graph Cuts,
IP(19), No. 11, November 2010, pp. 2849-2860.
IEEE DOI 1011
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Li, Q.[Qi], Ding, S.H.[Sheng-Hui], Yao, R.[Rui], Wang, Q.[Qi],
Real-time terahertz scanning imaging by use of a pyroelectric array camera and image denoising,
JOSA-A(27), No. 11, November 2010, pp. 2381-2386.
DOI Link 1011
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Pan, M.C.[Min-Cheng],
Image restoration through regularization based on error energy minimization,
IJIST(20), No. 4, December 2010, pp. 308-315.
DOI Link 1011
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Cao, Y.[Yang], Luo, Y.P.[Yu-Pin], Yang, S.Y.[Shi-Yuan],
Image Denoising Based on Hierarchical Markov Random Field,
PRL(32), No. 2, 15 January 2011, pp. 368-374.
Elsevier DOI 1101
Image denoising; Markov random field; Bayesian estimation BibRef

Giannoula, A.,
Classification-Based Adaptive Filtering for Multiframe Blind Image Restoration,
IP(20), No. 2, February 2011, pp. 382-390.
IEEE DOI 1102
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Li, X.,
Fine-Granularity and Spatially-Adaptive Regularization for Projection-Based Image Deblurring,
IP(20), No. 4, April 2011, pp. 971-983.
IEEE DOI 1103
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Su, L.Y.[Li-Yun], Liu, R.H.[Rui-Hua],
Blind Image Restoration With Modified CMA,
IJIG(11), No. 1, January 2011, pp. 403-413.
DOI Link 1108
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Shen, Z.W.[Zuo-Wei], Toh, K.C.[Kim-Chuan], Yun, S.W.[Sang-Woon],
An Accelerated Proximal Gradient Algorithm for Frame-Based Image Restoration via the Balanced Approach,
SIIMS(4), No. 2, 2011, pp. 573-596.
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Zuo, W.M.[Wang-Meng], Lin, Z.C.[Zhou-Chen],
A Generalized Accelerated Proximal Gradient Approach for Total-Variation-Based Image Restoration,
IP(20), No. 10, October 2011, pp. 2748-2759.
IEEE DOI 1110
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Chen, Q.[Qi], Wang, Y.F.[Yi-Fei], Geng, Z.Y.[Zheng-Yang], Wang, Y.[Yisen], Yang, J.S.[Jian-Sheng], Lin, Z.C.[Zhou-Chen],
Equilibrium Image Denoising With Implicit Differentiation,
IP(32), 2023, pp. 1868-1881.
IEEE DOI 2303
Iterative methods, Image denoising, Computational modeling, Training, Noise reduction, Mathematical models, Neural networks, deep equilibrium models BibRef

Geng, Y.L.[Yan-Lin], Lin, T.[Tong], Lin, Z.C.[Zhou-Chen], Hao, P.W.[Peng-Wei],
Refined Exponential Filter with Applications to Image Restoration and Interpolation,
ACCV09(III: 33-42).
Springer DOI 0909
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Lopez-Martinez, J.L.[Jose L.], Kober, V.[Vitaly],
Blind Adaptive Method for Image Restoration Using Microscanning,
IEICE(E95-D), No. 1, January 2012, pp. 280-284.
WWW Link. 1201
Multiple images. each a sample of the scene. BibRef

Zoubir, A.M., Koivunen, V., Chakhchoukh, Y., Muma, M.,
Robust Estimation in Signal Processing: A Tutorial-Style Treatment of Fundamental Concepts,
SPMag(29), No. 4, 2012, pp. 61-80.
IEEE DOI 1206
BibRef

Yousefi, S., Kehtarnavaz, N., Cao, Y., Razlighi, Q.R.,
Bilateral Markov mesh random field and its application to image restoration,
JVCIR(23), No. 7, October 2012, pp. 1051-1059.
Elsevier DOI 1209
Markov random fields; Bilateral Markov mesh random field; Image modeling; Image restoration; Image processing; Texture analysis; Causal Markov random field; Stochastic image analysis BibRef

Lu, C.W.,
Image restoration and decomposition using nonconvex non-smooth regularisation and negative Hilbert-Sobolev norm,
IET-IPR(6), No. 6, 2012, pp. 706-716.
DOI Link 1210
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Chen, X.J.[Xiao-Jun], Ng, M.K., Zhang, C.[Chao],
Non-Lipschitz L_p-Regularization and Box Constrained Model for Image Restoration,
IP(21), No. 12, December 2012, pp. 4709-4721.
IEEE DOI 1212
BibRef

Bian, W.[Wei], Chen, X.J.[Xiao-Jun],
Linearly Constrained Non-Lipschitz Optimization for Image Restoration,
SIIMS(8), No. 4, 2015, pp. 2294-2322.
DOI Link 1601
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Zhou, J.T.[Jian-Tao], Wu, X.L.[Xiao-Lin], Zhang, L.[Lei],
L_2 Restoration of L_inf -Decoded Images Via Soft-Decision Estimation,
IP(21), No. 12, December 2012, pp. 4797-4807.
IEEE DOI 1212
BibRef

Zachariah, D., Skog, I., Jansson, M., Handel, P.,
Bayesian Estimation With Distance Bounds,
SPLetters(19), No. 12, December 2012, pp. 880-883.
IEEE DOI 1212
BibRef

Zachariah, D., Stoica, P.,
Cramer-Rao Bound Analog of Bayes' Rule,
SPMag(32), No. 2, March 2015, pp. 164-168.
IEEE DOI 1503
Lecture Notes. Gaussian processes BibRef

Sun, J.[Jian], Tappen, M.F.[Marshall F.],
Separable Markov Random Field Model and Its Applications in Low Level Vision,
IP(22), No. 1, January 2013, pp. 402-407.
IEEE DOI 1301
BibRef
Earlier:
Learning non-local range Markov Random field for image restoration,
CVPR11(2745-2752).
IEEE DOI 1106
BibRef

Deng, L.J.[Liang-Jian], Huang, T.Z.[Ting-Zhu], Zhao, X.L.[Xi-Le], Zhao, L.[Liang], Wang, S.[Si],
Signal restoration combining Tikhonov regularization and multilevel method with thresholding strategy,
JOSA-A(30), No. 5, May 2013, pp. 948-955.
DOI Link 1305

See also Regularization of Ill-Posed Problems, The. BibRef

Yousefi, S., Kehtarnavaz, N., Cao, Y.[Yan],
Computationally Tractable Stochastic Image Modeling Based on Symmetric Markov Mesh Random Fields,
IP(22), No. 6, 2013, pp. 2192-2206.
IEEE DOI Markov processes; image restoration; Markov random fields 1307
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Wang, Y.Q.[Yi-Qing], Morel, J.M.[Jean-Michel],
SURE Guided Gaussian Mixture Image Denoising,
SIIMS(6), No. 2, 2013, pp. 999-1034.
DOI Link 1307
Implementation:
See also Implementation of SURE Guided Piecewise Linear Image Denoising, The. BibRef

Wang, Y.Q.[Yi-Qing],
The Implementation of SURE Guided Piecewise Linear Image Denoising,
IPOL(2012), No. 2012, pp. 52.
DOI Link 1309

See also SURE Guided Gaussian Mixture Image Denoising. BibRef

He, J.P.[Jin-Ping], Gao, K.[Kun], Ni, G.Q.[Guo-Qiang], Su, G.D.[Guang-Da], Chen, J.S.[Jian-Sheng],
Learning from Ideal Edge for Image Restoration,
IEICE(E96-D), No. 11, November 2013, pp. 2487-2491.
WWW Link. 1311
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Lanza, A.[Alessandro], Morigi, S.[Serena], Sgallari, F.[Fiorella], Yezzi, A.J.[Anthony J.],
Variational Image Denoising Based on Autocorrelation Whiteness,
SIIMS(6), No. 4, 2013, pp. 1931-1955.
DOI Link 1402
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Lanza, A.[Alessandro], Morigi, S.[Serena], Sciacchitano, F.[Federica], Sgallari, F.[Fiorella],
Whiteness Constraints in a Unified Variational Framework for Image Restoration,
JMIV(60), No. 9, November 2018, pp. 1503-1526.
Springer DOI 1810
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Lanza, A.[Alessandro], Morigi, S.[Serena], Sgallari, F.[Fiorella],
Variational Image Restoration with Constraints on Noise Whiteness,
JMIV(53), No. 1, September 2015, pp. 61-77.
Springer DOI 1505
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Lanza, A.[Alessandro], Morigi, S.[Serena], Sgallari, F.[Fiorella],
Convex Image Denoising via Non-convex Regularization with Parameter Selection,
JMIV(56), No. 2, October 2016, pp. 195-220.
Springer DOI 1609
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Earlier:
Convex Image Denoising via Non-Convex Regularization,
SSVM15(666-677).
Springer DOI 1506
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Selesnick, I.[Ivan], Lanza, A.[Alessandro], Morigi, S.[Serena], Sgallari, F.[Fiorella],
Non-convex Total Variation Regularization for Convex Denoising of Signals,
JMIV(62), No. 6-7, July 2020, pp. 825-841.
Springer DOI 2007
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Lanza, A.[Alessandro], Sciacchitano, F.[Federica], Morigi, S.[Serena], Sgallari, F.[Fiorella],
A Unified Framework for the Restoration of Images Corrupted by Additive White Noise,
SSVM17(498-510).
Springer DOI 1706
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Wang, Z., Zhu, J., Yan, F., Xie, M.,
Fidelity-Beltrami-Sparsity Model for Inverse Problems in Multichannel Image Processing,
SIIMS(6), No. 4, 2013, pp. 2685-2713.
DOI Link 1402
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Liu, X.M.[Xian-Ming], Zhai, D.M.[De-Ming], Zhao, D.B.[De-Bin], Zhai, G.T.[Guang-Tao], Gao, W.[Wen],
Progressive Image Denoising Through Hybrid Graph Laplacian Regularization: A Unified Framework,
IP(23), No. 4, April 2014, pp. 1491-1503.
IEEE DOI 1404
Laplace transforms BibRef

Moussallam, M., Gramfort, A., Daudet, L., Richard, G.,
Blind Denoising with Random Greedy Pursuits,
SPLetters(21), No. 11, November 2014, pp. 1341-1345.
IEEE DOI 1408
greedy algorithms BibRef

Kim, K., Lee, J.,
Nonlinear Dynamic Projection for Noise Reduction of Dispersed Manifolds,
PAMI(36), No. 11, November 2014, pp. 2303-2309.
IEEE DOI 1410
Algorithm design and analysis BibRef

Yousefian, N., Hansen, J.H.L., Loizou, P.C.,
A Hybrid Coherence Model for Noise Reduction in Reverberant Environments,
SPLetters(22), No. 3, March 2015, pp. 279-282.
IEEE DOI 1410
Coherence BibRef

Imre, E.[Evren], Hilton, A.[Adrian],
Covariance estimation for minimal geometry solvers via scaled unscented transformation,
CVIU(130), No. 1, 2015, pp. 18-34.
Elsevier DOI 1411
Covariance estimation BibRef

Zhao, S., Shmaliy, Y.S., Liu, F.,
Fast Computation of Discrete Optimal FIR Estimates in White Gaussian Noise,
SPLetters(22), No. 6, June 2015, pp. 718-722.
IEEE DOI 1411
Iterative algorithm BibRef

Sahoo, S.K., Makur, A.,
Enhancing Image Denoising by Controlling Noise Incursion in Learned Dictionaries,
SPLetters(22), No. 8, August 2015, pp. 1123-1126.
IEEE DOI 1502
image denoising BibRef

McGaffin, M.G., Fessler, J.A.[Jeffrey A.],
Edge-Preserving Image Denoising via Group Coordinate Descent on the GPU,
IP(24), No. 4, April 2015, pp. 1273-1281.
IEEE DOI 1503
graphics processing units BibRef

Kim, D.[Donghwan], Fessler, J.A.[Jeffrey A.],
An optimized first-order method for image restoration,
ICIP15(3675-3679)
IEEE DOI 1512
First-order methods BibRef

Yue, H.J.[Huan-Jing], Sun, X.Y.[Xiao-Yan], Yang, J.Y.[Jing-Yu], Wu, F.[Feng],
Image Denoising by Exploring External and Internal Correlations,
IP(24), No. 6, June 2015, pp. 1967-1982.
IEEE DOI 1504
BibRef
Earlier:
CID: Combined Image Denoising in Spatial and Frequency Domains Using Web Images,
CVPR14(2933-2940)
IEEE DOI 1409

See also Landmark Image Super-Resolution by Retrieving Web Images. Accuracy BibRef

Jalab, H.A.[Hamid A.], Ibrahim, R.W.[Rabha W.],
Fractional Conway Polynomials for Image Denoising with Regularized Fractional Power Parameters,
JMIV(51), No. 3, March 2015, pp. 442-450.
WWW Link. 1504
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Cohen, E.[Eliahu], Heiman, R.[Ron], Carmi, M.[Maya], Hadar, O.[Ofer], Cohen, A.[Asaf],
When physics meets signal processing: Image and video denoising based on Ising theory,
SP:IC(34), No. 1, 2015, pp. 14-21.
Elsevier DOI 1505
Image denoising BibRef

Jain, P.[Paras], Tyagi, V.[Vipin],
An adaptive edge-preserving image denoising technique using tetrolet transforms,
VC(31), No. 5, May 2015, pp. 657-674.
WWW Link. 1505
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Moallem, P.[Payman], Masoumzadeh, M.[Monire], Habibi, M.[Mehdi],
A novel adaptive Gaussian restoration filter for reducing periodic noises in digital image,
SIViP(9), No. 5, July 2015, pp. 1179-1191.
WWW Link. 1506
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Özkan, K., Seke, E.,
Image denoising using common vector approach,
IET-IPR(9), No. 8, 2015, pp. 709-715.
DOI Link 1506
estimation theory BibRef

Pham, T.D.,
Estimating Parameters of Optimal Average and Adaptive Wiener Filters for Image Restoration with Sequential Gaussian Simulation,
SPLetters(22), No. 11, November 2015, pp. 1950-1954.
IEEE DOI 1509
AWGN BibRef

Zeng, X., Bian, W., Liu, W., Shen, J., Tao, D.,
Dictionary Pair Learning on Grassmann Manifolds for Image Denoising,
IP(24), No. 11, November 2015, pp. 4556-4569.
IEEE DOI 1509
Dictionaries BibRef

Shen, X.Y.[Xiao-Yong], Yan, Q.[Qiong], Xu, L.[Li], Ma, L.Z.[Li-Zhuang], Jia, J.Y.[Jia-Ya],
Multispectral Joint Image Restoration via Optimizing a Scale Map,
PAMI(37), No. 12, December 2015, pp. 2518-2530.
IEEE DOI 1512
image colour analysis BibRef

Shen, X.Y.[Xiao-Yong], Zhou, C.[Chao], Xu, L.[Li], Jia, J.Y.[Jia-Ya],
Mutual-Structure for Joint Filtering,
IJCV(125), No. 1-3, December 2018, pp. 19-33.
Springer DOI 1711
BibRef
Earlier: ICCV15(3406-3414)
IEEE DOI 1602
Filter a target image for structural information from a reference image. BibRef

Yan, Q.[Qiong], Shen, X.Y.[Xiao-Yong], Xu, L.[Li], Zhuo, S.J.[Shao-Jie], Zhang, X.P.[Xiao-Peng], Shen, L.[Liang], Jia, J.Y.[Jia-Ya],
Cross-Field Joint Image Restoration via Scale Map,
ICCV13(1537-1544)
IEEE DOI 1403
cross-field; denoise; image restoration; near-infrared BibRef

Li, M.[Meng], Ghosal, S.,
Fast Translation Invariant Multiscale Image Denoising,
IP(24), No. 12, December 2015, pp. 4876-4887.
IEEE DOI 1512
Gaussian noise BibRef

Lu, X.[Xin], Lin, Z.[Zhe], Jin, H.L.[Hai-Lin], Yang, J.C.[Jian-Chao], Wang, J.Z.,
Image-Specific Prior Adaptation for Denoising,
IP(24), No. 12, December 2015, pp. 5469-5478.
IEEE DOI 1512
Gaussian processes BibRef

Lu, X.[Xin], Lin, Z.[Zhe], Jin, H.L.[Hai-Lin],
Tree-Based Locally Linear Regression for Image Denoising,
WACV15(472-479)
IEEE DOI 1503
Databases;Noise level;Noise measurement;Noise reduction;PSNR;Training BibRef

Portilla, J., Tristan-Vega, A., Selesnick, I.W.,
Efficient and Robust Image Restoration Using Multiple-Feature L2-Relaxed Sparse Analysis Priors,
IP(24), No. 12, December 2015, pp. 5046-5059.
IEEE DOI 1512
Convergence BibRef

Yu, L.[Lei], Wei, C.[Chen], Zheng, G.[Gang],
Adaptive Bayesian Estimation with Cluster Structured Sparsity,
SPLetters(22), No. 12, December 2015, pp. 2309-2313.
IEEE DOI 1512
adaptive estimation BibRef

Azghani, M., Karimi, M., Marvasti, F.,
Multihypothesis Compressed Video Sensing Technique,
CirSysVideo(26), No. 4, April 2016, pp. 627-635.
IEEE DOI 1604
Cost function
See also Regularization of Ill-Posed Problems, The. BibRef

Lei, Y.[Yang], Song, Z.J.[Zhan-Jie], Song, Q.W.[Qi-Wei],
Non-Convex Low-Rank Approximation for Image Denoising and Deblurring,
IEICE(E99-D), No. 5, May 2016, pp. 1364-1374.
WWW Link. 1605
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Al-Badrawi, M.H., Al-Jewad, B.Z., Smith, W.J., Kirsch, N.J.,
A De-noising Scheme Based on the Null Hypothesis of Intrinsic Mode Functions,
SPLetters(23), No. 7, July 2016, pp. 924-928.
IEEE DOI 1608
signal denoising BibRef

Smith, J.R., Al-Badrawi, M.H., Kirsch, N.J.,
An Optimized De-Noising Scheme Based on the Null Hypothesis of Intrinsic Mode Functions,
SPLetters(26), No. 8, August 2019, pp. 1232-1236.
IEEE DOI 1908
computational complexity, filtering theory, medical signal processing, Newton-Raphson method, noise, signal de-noising BibRef

Al-Badrawi, M.H., Kirsch, N.J., Al-Jewad, B.Z.,
Intrinsic Mode Function Based Noise Power Estimation With Applications to Semiblind Spectrum Sensing Methods,
SPLetters(24), No. 7, July 2017, pp. 1088-1092.
IEEE DOI 1706
Detectors, Eigenvalues and eigenfunctions, Estimation, Noise measurement, Signal to noise ratio, Empirical mode decomposition (EMD), intrinsic mode functions (IMFs), noise power estimation, semiblind detectors, spectrum, sensing BibRef

Dell'Acqua, P.[Pietro],
Nu-acceleration of statistical iterative methods for image restoration,
SIViP(10), No. 5, May 2016, pp. 927-934.
Springer DOI 1608
Landweber method. BibRef

Luo, E., Chan, S.H., Nguyen, T.Q.,
Adaptive Image Denoising by Mixture Adaptation,
IP(25), No. 10, October 2016, pp. 4489-4503.
IEEE DOI 1610
Bayes methods BibRef

Hong, H.Y.[Han-Yu], Hua, X.[Xia], Zhang, X.H.[Xiu-Hua], Shi, Y.[Yu],
Multi-frame real image restoration based on double loops with alternative maximum likelihood estimation,
SIViP(10), No. 8, November 2016, pp. 1489-1495.
WWW Link. 1610
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Ma, H.J.[Hong-Jin], Nie, Y.F.[Yu-Feng],
An edge fusion scheme for image denoising based on anisotropic diffusion models,
JVCIR(40, Part B), No. 1, 2016, pp. 406-417.
Elsevier DOI 1610
Image denoising BibRef

Dong, J.L.[Jun-Liang], Gao, J.B.[Jun-Bin], Ju, F.[Fujiao], Shen, J.H.[Jing-Hua],
Modulus Methods for Nonnegatively Constrained Image Restoration,
SIIMS(9), No. 3, 2016, pp. 1226-1246.
DOI Link 1610
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He, B.S.[Bing-Sheng], Ma, F.[Feng], Yuan, X.M.[Xiao-Ming],
Convergence Study on the Symmetric Version of ADMM with Larger Step Sizes,
SIIMS(9), No. 3, 2016, pp. 1467-1501.
DOI Link 1610
ADMM: alternating direction method of multipliers. Or: special split Bregman. BibRef

Peker, E.[Eli], Wiesel, A.[Ami],
Fitting Generalized Multivariate Huber Loss Functions,
SPLetters(23), No. 11, November 2016, pp. 1647-1651.
IEEE DOI 1609
least squares approximations BibRef

Hu, R., Fu, Y., Chen, Z., Xiang, Y., Rong, R.,
Robust Sparse Signal Recovery in the Presence of the S alpha-S Noise,
SPLetters(23), No. 11, November 2016, pp. 1687-1691.
IEEE DOI 1609
function approximation Symmetric alpha-stable noise. BibRef

Jakhetiya, V.[Vinit], Lin, W.S.[Wei-Si], Jaiswal, S.I.P.[Sun-Il P.], Guntuku, S.C.[Sharath Chandra], Au, O.C.[Oscar C.],
Maximum a Posterior and Perceptually Motivated Reconstruction Algorithm: A Generic Framework,
MultMed(19), No. 1, January 2017, pp. 93-106.
IEEE DOI 1612
Image edge detection BibRef

Pang, J.H.[Jia-Hao], Cheung, G.[Gene],
Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain,
IP(26), No. 4, April 2017, pp. 1770-1785.
IEEE DOI 1704
diffusion BibRef

Pierazzo, N.[Nicola], Facciolo, G.[Gabriele],
Data Adaptive Dual Domain Denoising: a Method to Boost State of the Art Denoising Algorithms,
IPOL(7), 2017, pp. 93-114.
DOI Link 1706
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Pierazzo, N.[Nicola], Rais, M.E., Morel, J.M.[Jean-Michel], Facciolo, G.[Gabriele],
DA3D: Fast and data adaptive dual domain denoising,
ICIP15(432-436)
IEEE DOI 1512
BibRef
Earlier: A1, A3, A4, Only:
Optimizing the Data Adaptive Dual Domain Denoising Algorithm,
CIARP15(358-365).
Springer DOI 1511
Data Adaptive BibRef

Pierazzo, N.[Nicola], Morel, J.M.[Jean-Michel], Facciolo, G.[Gabriele],
Multi-Scale DCT Denoising,
IPOL(7), 2017, pp. 288-308.
DOI Link 1712
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Facciolo, G.[Gabriele], Pierazzo, N.[Nicola], Morel, J.M.[Jean-Michel],
Conservative Scale Recomposition for Multiscale Denoising (The Devil is in the High Frequency Detail),
SIIMS(10), No. 3, 2017, pp. 1603-1626.
DOI Link 1710
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Arias, P.[Pablo], Morel, J.M.[Jean-Michel],
Video Denoising via Empirical Bayesian Estimation of Space-Time Patches,
JMIV(60), No. 1, January 2018, pp. 70-93.
Springer DOI 1801
BibRef

Pierazzo, N., Lebrun, M., Rais, M.E., Morel, J.M., Facciolo, G.,
Non-local dual image denoising,
ICIP14(813-817)
IEEE DOI 1502
Frequency-domain analysis BibRef

Wong, K.M.[Kin-Ming], Wong, T.T.[Tien-Tsin],
Blue noise sampling using an N-body simulation-based method,
VC(33), No. 6-8, June 2017, pp. 823-832.
WWW Link. 1706
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Cornel, D.[Daniel], Tobler, R.F.[Robert F.], Sakai, H.[Hiroyuki], Luksch, C.[Christian], Wimmer, M.[Michael],
Forced Random Sampling: Fast generation of importance-guided blue-noise samples,
VC(33), No. 6-8, June 2017, pp. 833-843.
Springer DOI 1706
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Yang, C.C., Guo, S.M., Tsai, J.S.H.,
Evolutionary Fuzzy Block-Matching-Based Camera Raw Image Denoising,
Cyber(47), No. 9, September 2017, pp. 2862-2871.
IEEE DOI 1708
Cameras, Encoding, Fuzzy logic, Fuzzy sets, Image denoising, Noise measurement, Noise reduction, Camera raw image, differential evolution, fuzzy logic system (FLS), image denoising BibRef

Liu, J.[Jing], Liu, R.J.[Rui-Jiao], Wang, Y.H.[Ying-Hui], Chen, J.L.[Jin-Lei], Yang, Y.J.[Ya-Jie], Ma, D.L.[Dou-Li],
Image denoising searching similar blocks along edge directions,
SP:IC(57), No. 1, 2017, pp. 33-45.
Elsevier DOI 1709
Similar block BibRef

Xu, J.T.[Jiang-Tao], Nie, H.F.[Hua-Feng], Nie, K.[Kaiming], Jin, W.M.[Wei-Min],
Fixed-pattern noise correction method based on improved moment matching for a TDI CMOS image sensor,
JOSA-A(34), No. 9, September 2017, pp. 1500-1510.
DOI Link 1709
Image reconstruction-restoration, Noise in imaging systems, Optical sensing and sensors BibRef

Boccuto, A.[Antonio], Gerace, I.[Ivan], Martinelli, F.[Francesca],
Half-Quadratic Image Restoration with a Non-parallelism Constraint,
JMIV(59), No. 2, October 2017, pp. 270-295.
Springer DOI 1709
BibRef

Wang, Q., Zhang, X., Wu, Y., Tang, L., Zha, Z.,
Nonconvex Weighted L_p Minimization Based Group Sparse Representation Framework for Image Denoising,
SPLetters(24), No. 11, November 2017, pp. 1686-1690.
IEEE DOI 1710
Dictionaries, Image denoising, Minimization, Noise measurement, Noise reduction, Sparse matrices, Adaptive patch search (APS), generalized soft-thresholding (GST) algorithm, group sparsity, image denoising, weighted, L_p, minimization BibRef

Diop, E.S.[El_Hadji S.], Alexandre, R.[Radjesvarane], Boudraa, A.O.[Abdel O.],
Two-Dimensional Curvature-Based Analysis of Intrinsic Mode Functions,
SPLetters(25), No. 1, January 2018, pp. 20-24.
IEEE DOI 1801
empirical mode decomposition (EMD). image processing, partial differential equations, 2D EMD algorithms, 2D curvature based analysis, partial differential equations. BibRef

Siadat, M.[Medya], Aghazadeh, N.[Nasser], Öktem, O.[Ozan],
Reordering for improving global Arnoldi-Tikhonov method in image restoration problems,
SIViP(12), No. 3, March 2018, pp. 497-504.
WWW Link. 1804
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Milanfar, P.[Peyman],
Rendition: Reclaiming What a Black Box Takes Away,
SIIMS(11), No. 4, 2018, pp. 2722-2756.
DOI Link 1901
Recover the image which has been modified by some unknown process. BibRef

Zeng, C.[Chao], Jia, R.[Rui], Wu, C.L.[Chun-Lin],
An Iterative Support Shrinking Algorithm for Non-Lipschitz Optimization in Image Restoration,
JMIV(61), No. 1, January 2019, pp. 122-139.
Springer DOI 1901
BibRef

Liu, Z.W.[Zhan-Wen], Gao, T.[Tao], Kong, F.J.[Fan-Jie], Jiao, Z.H.[Zi-Heng], Yang, A.[Aodong], Li, S.Y.[Shu-Ying], Liu, B.[Bo],
Restoration algorithm for noisy complex illumination,
IET-CV(13), No. 2, March 2019, pp. 224-232.
DOI Link 1902
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Baloch, G.[Gulsher], Ahmed, J.[Junaid],
Enhancing proximity measure between residual and noise for image denoising,
IJCVR(9), No. 1, 2019, pp. 56-69.
DOI Link 1903
BibRef

Ge, H.M.[Huan-Min], Wang, L.[Libo], Wen, J.M.[Jin-Ming], Xian, J.[Jun],
An RIP Condition for Exact Support Recovery With Covariance-Assisted Matching Pursuit,
SPLetters(26), No. 3, March 2019, pp. 520-524.
IEEE DOI 1903
covariance matrices, Gaussian noise, iterative methods, signal reconstruction, sparse matrices, RIP condition, sparse recovery BibRef

Jaouen, V., Bert, J., Boussion, N., Fayad, H., Hatt, M., Visvikis, D.,
Image Enhancement With PDEs and Nonconservative Advection Flow Fields,
IP(28), No. 6, June 2019, pp. 3075-3088.
IEEE DOI 1905
Image edge detection, Electric shock, Measurement, Image restoration, Image enhancement, AWGN, Smoothing methods, anisotropic diffusion BibRef

Li, S., Qin, B., Xiao, J., Liu, Q., Wang, Y., Liang, D.,
Multi-Channel and Multi-Model-Based Autoencoding Prior for Grayscale Image Restoration,
IP(29), No. 1, 2020, pp. 142-156.
IEEE DOI 1910
image colour analysis, image denoising, image restoration, iterative methods, learning (artificial intelligence), proximal gradient descent BibRef

Zhao, Q.[Qiang], Du, Q.Z.[Qi-Zhen], Sun, W.H.[Wen-Han], Chen, Y.K.[Yang-Kang],
Iterative Double Laplacian-Scaled Low-Rank Optimization for Under-Sampled and Noisy Signal Recovery,
GeoRS(57), No. 11, November 2019, pp. 9177-9187.
IEEE DOI 1911
Estimation, Data models, Noise measurement, Minimization, Analytical models, Optimization, Task analysis, Erratic noise, reweighted L1 minimization BibRef

Fan, S.[Sihui], Han, W.[Wei], Gao, Z.Q.[Zhi-Qiu], Yin, R.Y.[Ruo-Ying], Zheng, Y.[Yu],
Denoising Algorithm for the FY-4A GIIRS Based on Principal Component Analysis,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Zha, Z.Y.[Zhi-Yuan], Yuan, X.[Xin], Wen, B.H.[Bi-Han], Zhou, J.T.[Jian-Tao], Zhang, J.C.[Jia-Chao], Zhu, C.[Ce],
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration,
IP(29), 2020, pp. 3254-3269.
IEEE DOI 2002
Low-rank, rank residual constraint, nuclear norm minimization, nonlocal self-similarity, group-based sparse representation, image restoration BibRef

Zha, Z.Y.[Zhi-Yuan], Yuan, X.[Xin], Wen, B.H.[Bi-Han], Zhou, J.T.[Jian-Tao], Zhu, C.[Ce],
Group Sparsity Residual Constraint With Non-Local Priors for Image Restoration,
IP(29), 2020, pp. 8960-8975.
IEEE DOI 2009
Image restoration, Estimation, Minimization, Dictionaries, Task analysis, Adaptation models, Degradation, Image restoration, nonlocal self-similarity BibRef

Zha, Z.Y.[Zhi-Yuan], Wen, B.H.[Bi-Han], Yuan, X.[Xin], Zhou, J.T.Y.[Joey Tian-Yi], Zhou, J.T.[Jian-Tao], Zhu, C.[Ce],
Triply Complementary Priors for Image Restoration,
IP(30), 2021, pp. 5819-5834.
IEEE DOI 2106
Image restoration, Training, Task analysis, Image reconstruction, Degradation, Noise reduction, Inverse problems, Image restoration, hybrid plug-and-play BibRef

Zha, Z.Y.[Zhi-Yuan], Wen, B.H.[Bi-Han], Zhang, J.C.[Jia-Chao], Zhou, J.T.[Jian-Tao], Zhu, C.[Ce],
A Comparative Study for the Nuclear Norms Minimization Methods,
ICIP19(2050-2054)
IEEE DOI 1910
Low-rank matrix approximation, NNM, WNNM, GSR, image denoising BibRef

Wang, H., Li, Y., Cen, Y., He, Z.,
Multi-Matrices Low-Rank Decomposition With Structural Smoothness for Image Denoising,
CirSysVideo(30), No. 2, February 2020, pp. 349-361.
IEEE DOI 2002
Matrix decomposition, Sparse matrices, TV, Image reconstruction, Image restoration, Noise measurement, Optimization, matrices tri-factorization BibRef

Barbeiro, S.[Sílvia], Lobo, D.[Diogo],
Learning Stable Nonlinear Cross-Diffusion Models for Image Restoration,
JMIV(62), No. 2, February 2020, pp. 223-237.
Springer DOI 2002
BibRef

Fernández-Menduiña, S.[Samuel], Pérez-González, F.[Fernando],
Temporal Localization of Non-Static Digital Videos Using the Electrical Network Frequency,
SPLetters(27), 2020, pp. 745-749.
IEEE DOI 2006
underlying signal in video. Videos, Estimation, Frequency estimation, Phase locked loops, Frequency modulation, Demodulation, Time-frequency analysis, ENF, non-static videos BibRef

Wu, G.M.[Ge-Ming], Luo, S.Q.[Shu-Qian], Yang, Z.[Zhi],
Optimal weighted bilateral filter with dual-range kernel for Gaussian noise removal,
IET-IPR(14), No. 9, 20 July 2020, pp. 1840-1850.
DOI Link 2007
BibRef

Liu, R.S.[Ri-Sheng], Mu, P.[Pan], Chen, J.[Jian], Fan, X.[Xin], Luo, Z.X.[Zhong-Xuan],
Investigating Task-Driven Latent Feasibility for Nonconvex Image Modeling,
IP(29), 2020, pp. 7629-7640.
IEEE DOI 2007
Low-level vision, nonconvex image modeling, task-driven feasibility, maximum a posterior BibRef

Valsesia, D.[Diego], Fracastoro, G.[Giulia], Magli, E.[Enrico],
Deep Graph-Convolutional Image Denoising,
IP(29), 2020, pp. 8226-8237.
IEEE DOI 2008
BibRef
Earlier:
Image Denoising with Graph-Convolutional Neural Networks,
ICIP19(2399-2403)
IEEE DOI 1910
Convolution, Noise reduction, Image denoising, Biological neural networks, graph convolution. BibRef

Gong, X.[Xiao], Chen, W.[Wei], Chen, J.[Jie], Ai, B.[Bo],
Tensor Denoising Using Low-Rank Tensor Train Decomposition,
SPLetters(27), 2020, pp. 1685-1689.
IEEE DOI 1806
Tensile stress, Noise reduction, Matrix decomposition, Indexes, Signal processing, Machine learning, tensor decomposition BibRef

Tian, X.[Xin], Chen, W.[Wei], Zhao, F.[Fang], Li, B.[Bo], Wang, Z.Y.[Zhong-Yuan],
Robust CBCT Reconstruction Based On Low-Rank Tensor Decomposition And Total Variation Regularization,
ICIP20(330-334)
IEEE DOI 2011
Image reconstruction, TV, Tensile stress, Robustness, total variation BibRef

Ou, Y.[Yang], Luo, J.Q.[Jian-Qiao], Li, B.L.[Bai-Lin], Swamy, M.N.S.,
Gray-level image denoising with an improved weighted sparse coding,
JVCIR(72), 2020, pp. 102895.
Elsevier DOI 2010
Image denoising, Nonlocal self-similarity, Weight matrix, Weighted sparse coding BibRef

Wang, Y.Y.[Yi-Yang], Ma, L.[Long], Liu, R.S.[Ri-Sheng],
A convergent framework with learnable feasibility for Hadamard-based image recovery,
CVIU(202), 2021, pp. 103095.
Elsevier DOI 2012
Image recovery, Hadamard product, Data-driven feasibility, Learning paradigm, Coordinate update BibRef

Xiao, J.[Jie], Jin, Z.[Zhi], Zhang, H.R.[Huan-Rong],
A general model compression method for image restoration network,
SP:IC(93), 2021, pp. 116134.
Elsevier DOI 2103
Model compression, Image restoration, Deformable convolution kernel, Attention mechanism BibRef

Gao, Q.X.[Quan-Xue], Zhang, P.[Pu], Xia, W.[Wei], Xie, D.[Deyan], Gao, X.B.[Xin-Bo], Tao, D.C.[Da-Cheng],
Enhanced Tensor RPCA and its Application,
PAMI(43), No. 6, June 2021, pp. 2133-2140.
IEEE DOI 2106
Tensors, Minimization, Image color analysis, Principal component analysis, Periodic structures, multidimensional image denoising BibRef

Zha, Z.Y.[Zhi-Yuan], Wen, B.[Bihan], Yuan, X.[Xin], Zhou, J.T.[Jian-Tao], Zhu, C.[Ce],
Image Restoration via Reconciliation of Group Sparsity and Low-Rank Models,
IP(30), 2021, pp. 5223-5238.
IEEE DOI 2106
Image restoration, Image coding, Dictionaries, Adaptation models, Minimization, Image denoising, Sparse matrices, image restoration BibRef

Pei, Y.T.[Yan-Ting], Huang, Y.P.[Ya-Ping], Zhang, X.Y.[Xing-Yuan],
Consistency Guided Network for Degraded Image Classification,
CirSysVideo(31), No. 6, June 2021, pp. 2231-2246.
IEEE DOI 2106
Visualization, Image resolution, Image restoration, Semantics, Task analysis, Heating systems, Degradation, Image classification, visual attention alignment loss BibRef

Premnath, S.P., Renjit, J.A.[J. Arokia],
Image restoration model using Jaya-Bat optimization-enabled noise prediction map,
IET-IPR(15), No. 9, 2021, pp. 1926-1939.
DOI Link 2106
BibRef

Malladi, S.R.S.P.[Sree Ramya S. P.], Ram, S.[Sundaresh], Rodríguez, J.J.[Jeffrey J.],
Image Denoising Using Superpixel-Based PCA,
MultMed(23), 2021, pp. 2297-2309.
IEEE DOI 2108
Principal component analysis, Noise reduction, Noise measurement, Transforms, Image denoising, Decorrelation, Noise level, patch-based method BibRef

Zhou, R.[Rui], Han, J.T.[Jiang-Tao], Guo, Z.Y.[Zhen-Yu], Li, T.L.[Tong-Lin],
De-Noising of Magnetotelluric Signals by Discrete Wavelet Transform and SVD Decomposition,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Ko, K.[Keunsoo], Koh, Y.J.[Yeong Jun], Kim, C.S.[Chang-Su],
Blind and Compact Denoising Network Based on Noise Order Learning,
IP(31), 2022, pp. 1657-1670.
IEEE DOI 2202
Noise reduction, Feature extraction, Noise measurement, Image denoising, Complexity theory, Training, Noise level, convolutional neural network BibRef

Monma, Y.[Yuki], Aro, K.[Kan], Yasuda, M.[Muneki],
Hierarchical Gaussian Markov Random Field for Image Denoising,
IEICE(E105-D), No. 3, March 2022, pp. 689-699.
WWW Link. 2203
BibRef

Li, P.L.[Peng-Liang], Liang, J.L.[Jun-Li], Zhang, M.[Miaohua], Fan, W.[Wen], Yu, G.Y.[Guo-Yang],
Joint image denoising with gradient direction and edge-preserving regularization,
PR(125), 2022, pp. 108506.
Elsevier DOI 2203
Joint image denoising, Gradient direction, Majorization minimization, Nonlinear optimization, Nonconvex optimization BibRef

Mou, C.[Chong], Zhang, J.[Jian], Fan, X.P.[Xiao-Peng], Liu, H.F.[Hang-Fan], Wang, R.G.[Rong-Gang],
COLA-Net: Collaborative Attention Network for Image Restoration,
MultMed(24), 2022, pp. 1366-1377.
IEEE DOI 2204
Image restoration, Task analysis, Collaboration, Image denoising, Finite element analysis, Training, image restoration BibRef

Tomita, K.[Keigo], Tsutake, C.[Chihiro], Takahashi, K.[Keita], Fujii, T.[Toshiaki],
Denoising multi-view images by soft thresholding: A short-time DFT approach,
SP:IC(105), 2022, pp. 116710.
Elsevier DOI 2205
Multi-view images, Denoising, Short-time DFT, Soft thresholding, Model selection BibRef

Abedini, M.[Maryam], Haddad, H.[Horriyeh], Masouleh, M.F.[Marzieh Faridi], Shahbahrami, A.[Asadollah],
Image Denoising Using Sparse Representation and Principal Component Analysis,
IJIG(22), No. 4, July 2022, pp. 2250033.
DOI Link 2208
BibRef

Yin, H.T.[Hai-Tao], Ma, S.Y.[Si-Yuan],
CSformer: Cross-Scale Features Fusion Based Transformer for Image Denoising,
SPLetters(29), 2022, pp. 1809-1813.
IEEE DOI 2209
Transformers, Feature extraction, Convolution, Noise measurement, Image denoising, Noise reduction, Noise level, Cross-scale, transformer BibRef

Pan, X.G.[Xin-Gang], Zhan, X.H.[Xiao-Hang], Dai, B.[Bo], Lin, D.[Dahua], Loy, C.C.[Chen Change], Luo, P.[Ping],
Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation,
PAMI(44), No. 11, November 2022, pp. 7474-7489.
IEEE DOI 2210
BibRef
Earlier: ECCV20(II:262-277).
Springer DOI 2011
Image reconstruction, Generative adversarial networks, Image restoration, Generators, Task analysis, Image color analysis BibRef

Yu, Z.[Zhi], Luo, Y.H.[Yi-Hao], Liu, Z.F.[Zhi-Fa], Zhou, G.X.[Guo-Xu],
Contour information regularized tensor ring completion for realistic image restoration,
IET-IPR(16), No. 13, 2022, pp. 3499-3506.
DOI Link 2210
BibRef

Wu, D.[Di], Zhang, T.[Tao],
Corrected rank residual constraint model for image denoising,
IET-IPR(16), No. 13, 2022, pp. 3659-3668.
DOI Link 2210
BibRef

Xu, S.P.[Shao-Ping], Chen, X.J.[Xiao-Jun], Luo, J.[Jie], Cheng, X.H.[Xiao-Hui], Xiao, N.[Nan],
An unsupervised fusion network for boosting denoising performance,
JVCIR(88), 2022, pp. 103626.
Elsevier DOI 2210
Merge multiple denoising results. Image denoising, Boosting denoising performance, Deep fusion network, Unsupervised training strategy BibRef

He, J.W.[Jing-Wen], Dong, C.[Chao], Liu, Y.H.[Yi-Hao], Qiao, Y.[Yu],
Interactive Multi-Dimension Modulation for Image Restoration,
PAMI(44), No. 12, December 2022, pp. 9363-9379.
IEEE DOI 2212
Degradation, Modulation, Image restoration, Task analysis, Noise reduction, Estimation, Adaptation models, Image restoration, deep learning BibRef

Punnappurath, A.[Abhijith], Brown, M.S.[Michael S.],
A Little Bit More: Bitplane-Wise Bit-Depth Recovery,
PAMI(44), No. 12, December 2022, pp. 9718-9724.
IEEE DOI 2212
Training, Quantization (signal), Deep learning, Image restoration, Standards, Task analysis, Network architecture, Bit depth, bitplane, image restoration BibRef

Gajamannage, K.[Kelum], Park, Y.G.[Yong-Gi], Sadovski, A.[Alexey],
Geodesic Gramian denoising applied to the images contaminated with noise sampled from diverse probability distributions,
IET-IPR(17), No. 1, 2023, pp. 144-156.
DOI Link 2301
BibRef

Zhang, Z.W.[Zhen-Wei], Chen, K.[Ke], Tang, K.[Ke], Duan, Y.P.[Yu-Ping],
Fast Multi-Grid Methods for Minimizing Curvature Energies,
IP(32), 2023, pp. 1716-1731.
IEEE DOI 2303
Minimization, Computational modeling, Image restoration, Image reconstruction, Convergence, Surface treatment BibRef

Pan, Y.Z.[Yi-Zhong], Ren, C.[Chao], Wu, X.H.[Xiao-Hong], Huang, J.[Jie], He, X.H.[Xiao-Hai],
Real Image Denoising via Guided Residual Estimation and Noise Correction,
CirSysVideo(33), No. 4, April 2023, pp. 1994-2000.
IEEE DOI 2304
Noise reduction, Noise level, Noise measurement, Image denoising, Iterative methods, Image reconstruction, Estimation, guided residual estimation BibRef

Liu, J.[Jun], Liu, R.W.[Ryan Wen], Sun, J.N.[Jia-Ning], Zeng, T.Y.[Tie-Yong],
Rank-One Prior: Real-Time Scene Recovery,
PAMI(45), No. 7, July 2023, pp. 8845-8860.
IEEE DOI 2306
BibRef
Earlier:
Rank-One Prior: Toward Real-Time Scene Recovery,
CVPR21(14797-14805)
IEEE DOI 2111
Image color analysis, Image enhancement, Imaging, Image restoration, Scattering, Learning systems, Attenuation, Haze, unified spectrum. Learning systems, Visualization, Estimation, Training data, Video surveillance BibRef

Shen, L.[Lili], Zhao, B.[Bo], Li, Q.[Qunxia], Zhang, C.[Chuhe], Sun, X.[Xichun], Peng, B.[Bo],
Local to non-local: Multi-scale progressive attention network for image restoration,
CVIU(233), 2023, pp. 103725.
Elsevier DOI 2307
Image restoration, Convolutional neural networks, Single-scale feature enhancement, Multi-scale feature fusion, Window-dilation self-attention BibRef

Liu, L.[Lin], Xie, L.X.[Ling-Xi], Zhang, X.P.[Xiao-Peng], Yuan, S.X.[Shan-Xin], Chen, X.Y.[Xiang-Yu], Zhou, W.G.[Wen-Gang], Li, H.Q.[Hou-Qiang], Tian, Q.[Qi],
TAPE: Task-Agnostic Prior Embedding for Image Restoration,
ECCV22(XVIII:447-464).
Springer DOI 2211
BibRef

Jiang, Y.F.[Yi-Fan], Wronski, B.[Bartlomiej], Mildenhall, B.[Ben], Barron, J.T.[Jonathan T.], Wang, Z.Y.[Zhang-Yang], Xue, T.F.[Tian-Fan],
Fast and High Quality Image Denoising via Malleable Convolution,
ECCV22(XVIII:429-446).
Springer DOI 2211
BibRef

Zhang, Z.X.[Zhong-Xing], Liu, H.[Hui], Guo, Q.[Qiang],
Image Restoration Using Probability-Inducing Nuclear Norm Minimization,
ICIP22(2666-2670)
IEEE DOI 2211
Image quality, Codes, Superresolution, Noise reduction, Minimization, Approximation algorithms, Image restoration, Image restoration, nuclear norm minimization BibRef

Li, B.[Boyun], Liu, X.[Xiao], Hu, P.[Peng], Wu, Z.Q.[Zhong-Qin], Lv, J.C.[Jian-Cheng], Peng, X.[Xi],
All-In-One Image Restoration for Unknown Corruption,
CVPR22(17431-17441)
IEEE DOI 2210
Degradation, Photography, Codes, Image restoration, Pattern recognition, Low-level vision, Computational photography BibRef

Shin, W.[Wooksu], Ahn, N.[Namhyuk], Moon, J.H.[Jeong-Hyeon], Sohn, K.A.[Kyung-Ah],
Exploiting Distortion Information for Multi-degraded Image Restoration,
NTIRE22(536-545)
IEEE DOI 2210
Training, Knowledge engineering, Image recognition, Fuses, Supervised learning, Distortion, Image restoration BibRef

Choi, J.Y.[Joo-Young], Lee, J.[Jungbeom], Shin, C.[Chaehun], Kim, S.[Sungwon], Kim, H.W.[Hyun-Woo], Yoon, S.[Sungroh],
Perception Prioritized Training of Diffusion Models,
CVPR22(11462-11471)
IEEE DOI 2210
Training, Visualization, Computational modeling, Noise reduction, Data models, Image restoration, Pattern recognition, Image and video synthesis and generation BibRef

Lee, H.[Hunsang], Choi, H.[Hyesong], Sohn, K.H.[Kwang-Hoon], Min, D.B.[Dong-Bo],
KNN Local Attention for Image Restoration,
CVPR22(2129-2139)
IEEE DOI 2210
Image color analysis, Focusing, Machine learning, Transformers, Image restoration, Pattern recognition, Task analysis, Machine learning BibRef

Zhang, Y.[Yi], Li, D.[Dasong], Law, K.L.[Ka Lung], Wang, X.G.[Xiao-Gang], Qin, H.W.[Hong-Wei], Li, H.S.[Hong-Sheng],
IDR: Self-Supervised Image Denoising via Iterative Data Refinement,
CVPR22(2088-2097)
IEEE DOI 2210
Training, Codes, Noise reduction, Benchmark testing, Approximation algorithms, Pattern recognition, Low-level vision, Datasets and evaluation BibRef

Koh, J.[Jaihyun], Lee, J.[Jangho], Yoon, S.[Sungroh],
BNUDC: A Two-Branched Deep Neural Network for Restoring Images from Under-Display Cameras,
CVPR22(1940-1949)
IEEE DOI 2210
Degradation, Deep learning, Training, Image color analysis, Neural networks, Transforms, Cameras, Low-level vision, Vision applications and systems BibRef

Dutta, S.[Sayantan], Basarab, A.[Adrian], Georgeot, B.[Bertrand], Kouamé, D.[Denis],
Image Denoising Inspired by Quantum Many-Body physics,
ICIP21(1619-1623)
IEEE DOI 2201
Wavelet transforms, Quantum computing, AWGN, Noise reduction, Redundancy, Quantum mechanics, Filtering theory, quantum image processing. BibRef

Kawar, B.[Bahjat], Vaksman, G.[Gregory], Elad, M.[Michael],
Stochastic Image Denoising by Sampling from the Posterior Distribution,
AIM21(1866-1875)
IEEE DOI 2112
Uncertainty, Heuristic algorithms, Noise reduction, Measurement uncertainty, Stochastic processes, Size measurement BibRef

Ke, J.J.[Jun-Jie], Wang, Q.F.[Qi-Fei], Wang, Y.L.[Yi-Lin], Milanfar, P.[Peyman], Yang, F.[Feng],
MUSIQ: Multi-Scale Image Quality Transformer,
ICCV21(5128-5137)
IEEE DOI 2203
Image quality, Training, Degradation, Visualization, Shape, Image representation, Transformers, Vision applications and systems BibRef

Ohayon, G.[Guy], Adrai, T.[Theo], Vaksman, G.[Gregory], Elad, M.[Michael], Milanfar, P.[Peyman],
High Perceptual Quality Image Denoising with a Posterior Sampling CGAN,
AIM21(1805-1813)
IEEE DOI 2112
Deep learning, Noise reduction, Force, Distortion, Generative adversarial networks, Generators BibRef

Liu, Y.[Yang], Qin, Z.[Zhenyue], Anwar, S.[Saeed], Ji, P.[Pan], Kim, D.[Dongwoo], Caldwell, S.[Sabrina], Gedeon, T.[Tom],
Invertible Denoising Network: A Light Solution for Real Noise Removal,
CVPR21(13360-13369)
IEEE DOI 2111
Codes, Computational modeling, Noise reduction, Transforms, Image restoration, Pattern recognition BibRef

Mo, H.C.[Hong-Cheng], Jiang, J.F.[Jian-Fei], Wang, Q.[Qin], Yin, D.[Dong], Dong, P.Y.[Peng-Yu], Tian, J.J.[Jing-Jun],
Frequency Attention Network: Blind Noise Removal for Real Images,
ACCV20(II:168-184).
Springer DOI 2103
BibRef

Zhu, M.Z.[Ming-Zhu], Gao, Z.[Zhang], Yu, J.Z.[Jun-Zhi], He, B.W.[Bing-Wei], Liu, J.T.[Jian-Tao],
ALRe: Outlier Detection for Guided Refinement,
ECCV20(VII:788-802).
Springer DOI 2011
BibRef

Li, X.[Xin], Jin, X.[Xin], Lin, J.X.[Jian-Xin], Liu, S.[Sen], Wu, Y.J.[Yao-Jun], Yu, T.[Tao], Zhou, W.[Wei], Chen, Z.B.[Zhi-Bo],
Learning Disentangled Feature Representation for Hybrid-Distorted Image Restoration,
ECCV20(XXIX: 313-329).
Springer DOI 2010
image that is degraded by multiple distortions. BibRef

Wang, W., Guo, R., Tian, Y., Yang, W.,
CFSNet: Toward a Controllable Feature Space for Image Restoration,
ICCV19(4139-4148)
IEEE DOI 2004
Code, Image Restoration.
WWW Link. image reconstruction, image resolution, image restoration, learning (artificial intelligence), Distortion BibRef

Anwar, S., Barnes, N.,
Real Image Denoising With Feature Attention,
ICCV19(3155-3164)
IEEE DOI 2004
convolutional neural nets, image denoising, image denoising, feature attention, convolutional neural networks, Computational modeling BibRef

Potts, C., Yang, L., Oyen, D., Wohlberg, B.,
A Topological Graph-Based Representation for Denoising Low Quality Binary Images,
SGRL19(1788-1798)
IEEE DOI 2004
feature extraction, graph theory, image denoising, image resolution, learning (artificial intelligence), image representation BibRef

Marinc, T., Srinivasan, V., Gül, S., Hellge, C., Samek, W.,
Multi-Kernel Prediction Networks for Denoising of Burst Images,
ICIP19(2404-2408)
IEEE DOI 1910
Burst Image Denoising, Kernel Fusion, Deep Learning, Kernel Prediction Network BibRef

Kim, Y., Soh, J.W., Cho, N.I.,
Adaptively Tuning a Convolutional Neural Network by Gating Process for Image Denoising,
ICIP19(1800-1804)
IEEE DOI 1910
Denoising, Convolutional Neural Network, Adaptive Denoising, Gated Denoising, Noise Level Gated Denoising BibRef

Lempitsky, V., Vedaldi, A., Ulyanov, D.,
Deep Image Prior,
CVPR18(9446-9454)
IEEE DOI 1812
Image restoration, Image resolution, Noise reduction, Task analysis, Optimization, Generators, Image reconstruction BibRef

Dorta, G.[Garoe], Vicente, S.[Sara], Agapito, L.[Lourdes], Campbell, N.D.F.[Neill D. F.], Simpson, I.[Ivor],
Structured Uncertainty Prediction Networks,
CVPR18(5477-5485)
IEEE DOI 1812
Image reconstruction, Uncertainty, Covariance matrices, Predictive models, Data models, Sparse matrices, Estimation BibRef

Chen, J.W.[Jing-Wen], Chen, J.W.[Jia-Wei], Chao, H.Y.[Hong-Yang], Yang, M.[Ming],
Image Blind Denoising with Generative Adversarial Network Based Noise Modeling,
CVPR18(3155-3164)
IEEE DOI 1812
Noise reduction, Generative adversarial networks, Noise measurement, Training, Adaptation models BibRef

Choi, S., Isidoro, J., Getreuer, P., Milanfar, P.,
Fast, Trainable, Multiscale Denoising,
ICIP18(963-967)
IEEE DOI 1809
Noise reduction, Noise measurement, Kernel, Tensile stress, Pipelines, Noise level, Blades, Image denoising, filter learning, multiscale BibRef

Wang, H.M.[He-Ming], Mann, R.[Richard], Vrscay, E.R.[Edward R.],
A Diffusion-Based Two-Dimensional Empirical Mode Decomposition (EMD) Algorithm for Image Analysis,
ICIAR18(295-305).
Springer DOI 1807
BibRef

Channoufi, I.[Ines], Bourouis, S.[Sami], Bouguila, N.[Nizar], Hamrouni, K.[Kamel],
A Flexible Statistical Model for Image Denoising,
ICIAR18(30-38).
Springer DOI 1807
BibRef

Bergmann, R.,
MVIRT, a toolbox for manifold-valued image restoration,
ICIP17(201-205)
IEEE DOI 1803
Computational modeling, Image restoration, Manifolds, Minimization, TV, Task analysis, Douglas-Rachford splitting, Riemannian manifold, variational models BibRef

Chen, P.[Peng], Wu, S.Q.[Shi-Qian], Fang, H.P.[Hong-Ping], Chen, B.[Bin], Wang, W.[Wei],
Gaussian Noise Detection and Adaptive Non-local Means Filter,
PSIVT17(396-405).
Springer DOI 1802
BibRef

Zhang, J., Pan, J., Lai, W.S., Lau, R.W.H., Yang, M.H.,
Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution,
CVPR17(6969-6977)
IEEE DOI 1711
Convolution, Deconvolution, Image denoising, Image restoration, Kernel, Noise reduction, Training BibRef

Foare, M., Lachaud, J.O., Talbot, H.,
Image restoration and segmentation using the Ambrosio-Tortorelli functional and Discrete Calculus,
ICPR16(1418-1423)
IEEE DOI 1705
Calculus, Convergence, Image restoration, Image segmentation, Minimization, Noise reduction, Standards, Mumford-Shah functional, denoising, inverse problems, optimisation, segmentation, simplification, variational, formulation BibRef

Li, J., Tu, Q.A.[Qi-Ang], Yan, Z.Y.[Zi-Ye],
Refining pre-image via error compensation for KPCA-based pattern de-noising,
ICPR16(414-419)
IEEE DOI 1705
Error compensation, Image denoising, Kernel, Noise reduction, Principal component analysis, Systematics, Training, error compensation, kernel principal component analysis (KPCA), pre-image, systematic, error BibRef

Boudjenouia, F., Abed-Meraim, K., Chetouani, A., Jennane, R.,
On the use of image quality measures for image restoration,
IPTA16(1-6)
IEEE DOI 1703
gradient methods BibRef

Li, J.W.[Jian-Wei], Chen, X.W.[Xiao-Wu], Zou, D.Q.[Dong-Qing], Gao, B.[Bo], Teng, W.[Wei],
Conformal and Low-Rank Sparse Representation for Image Restoration,
ICCV15(235-243)
IEEE DOI 1602
Dictionaries BibRef

Guven, H.E.[H. Emre], Gungor, A.[Alper], Cetin, M.[Mujdat],
An Augmented Lagrangian Method for image reconstruction with multiple features,
ICIP15(4175-4179)
IEEE DOI 1512
Alternating Direction Method of Multipliers BibRef

Shah, T., Shikkenawis, G.[Gitam], Mitra, S.K.[Suman K.],
Epitome based transform domain Image Denoising,
ICAPR15(1-6)
IEEE DOI 1511
image denoising BibRef

Aouinti, F., Nasri, M., Moussaoui, M., Bouali, B.,
Image restoration by applying the genetic approach to the iterative Wiener filter,
ISCV15(1-5)
IEEE DOI 1506
Wiener filters BibRef

Kandaswamy, C.[Chetak], Silva, L.M.[Luís M.], Cardoso, J.S.[Jaime S.],
Source-Target-Source Classification Using Stacked Denoising Autoencoders,
IbPRIA15(39-47).
Springer DOI 1506
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Liu, K.[Ke], Xu, M.[Ming], Yu, Z.Y.[Ze-Yun],
Feature-Preserving Image Restoration from Adaptive Triangular Meshes,
Restoration14(II: 31-46).
Springer DOI 1504
BibRef

Jiang, F.[Feng], Zhang, S.P.[Sheng-Ping], Zhao, D.B.[De-Bin], Kung, S.Y.,
Image Restoration via Multi-prior Collaboration,
ACCV14(III: 191-204).
Springer DOI 1504
BibRef

Zang, X.H.[Xiu-Huan], Shi, Y.H.[Yun-Hui], Wang, J.[Jin], Ding, W.P.[Wen-Peng], Yin, B.C.[Bao-Cai],
Optimizing collaborative sparse dictionary for compressive light field photography,
VCIP16(1-4)
IEEE DOI 1701
Algorithm design and analysis BibRef

Wang, J.[Jin], Cai, J.F.[Jian-Feng], Shi, Y.H.[Yun-Hui], Yin, B.C.[Bao-Cai],
Incoherent dictionary learning for sparse representation based image denoising,
ICIP14(4582-4586)
IEEE DOI 1502
Coherence BibRef

McCrackin, L.[Laura], Shirani, S.[Shahram],
Strategic image denoising using a support vector machine with seam energy and saliency features,
ICIP14(2684-2688)
IEEE DOI 1502
Image color analysis BibRef

Zhang, X.[Xing], Lyu, S.W.[Si-Wei],
Using Projection Kurtosis Concentration of Natural Images for Blind Noise Covariance Matrix Estimation,
CVPR14(2870-2876)
IEEE DOI 1409
natural image statistics BibRef

Anantrasirichai, N., Burn, J., Bull, D.R.[David R.],
Projective image restoration using sparsity regularization,
ICIP13(1080-1084)
IEEE DOI 1402
Accuracy BibRef

Muramatsu, S.[Shogo], Aizawa, N.[Natsuki],
Image restoration with 2-D non-separable oversampled lapped transforms,
ICIP13(1051-1055)
IEEE DOI 1402
Dictionaries BibRef

Feng, J.Z.[Jian-Zhou], Song, L.[Li], Huo, X.M.[Xiao-Ming], Yang, X.K.[Xiao-Kang], Zhang, W.J.[Wen-Jun],
Image restoration via efficient Gaussian mixture model learning,
ICIP13(1056-1060)
IEEE DOI 1402
Dictionaries BibRef

Lee, C.[Chul], Kim, C.S.[Chang-Su], Lee, S.U.[Sang-Uk],
Probabilistic depth-guided multi-view image denoising,
ICIP13(905-908)
IEEE DOI 1402
Estimation BibRef

Wang, H.Z.[Han-Zi], Cai, J.L.[Jin-Long], Tang, J.Y.[Jian-Yu],
AMSAC: An adaptive robust estimator for model fitting,
ICIP13(305-309)
IEEE DOI 1402
Adaptation models robust scale estimator called AIKOSE. Scale of noise. BibRef

Mobasheri, M.R., Zendehbad, S.A.,
Diagnosis and Repair of Random Noise in The Sensor's Chris-Proba,
SMPR13(463-468).
HTML Version. 1311
CHRIS sensor on the PROBA-1 satellite. BibRef

Matakos, A.[Antonios], Ramani, S.[Sathish], Fessler, J.A.[Jeffrey A.],
Image restoration using non-circulant shift-invariant system models,
ICIP12(3061-3064).
IEEE DOI 1302
BibRef

Pan, J.F.[Jing-Feng], Cao, T.Y.[Tie-Yong], Zhang, X.W.[Xiong-Wei], Huang, H.[Hui],
A quantum-inspired noise reduction method based on noise feature codebook,
CVRS12(158-163).
IEEE DOI 1302
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Sa, P.K.[Pankaj Kumar], Majhi, B.[Banshidhar],
Adaptive edge preserving regularized image restoration,
ICIIP11(1-5).
IEEE DOI 1112
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Nguyen, T.A.[Tuan-Anh], Hong, M.C.[Min-Cheol],
Filtering-Based Noise Estimation for Denoising the Image Degraded by Gaussian Noise,
PSIVT11(II: 157-167).
Springer DOI 1111
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Lobry, S., Denis, L., Tupin, F.,
Sparse-smooth decomposition models for multi-temporal SAR images,
MultiTemp15(1-4)
IEEE DOI 1511
image denoising BibRef

Denis, L., Tupin, F., Rondeau, X.,
Exact discrete minimization for TV+L0 image decomposition models,
ICIP10(2525-2528).
IEEE DOI 1009
Noise. Decompose and denoise each separately. BibRef

Zhao, S.P.[Shuang-Ping],
A combined image denoising method,
IASP10(254-256).
IEEE DOI 1004
Wiener filter in wavelet and spatial domains. BibRef

Chountasis, S., Pappas, D., Katsikis, V.N.,
Image Restoration Via Fast Computing of the Moore-Penrose Inverse Matrix,
WSSIP09(1-4).
IEEE DOI 0906
BibRef

Huang, Y.L.[Yong-Lin], Ye, Y.T.[Yu-Tang], Qiao, N.S.[Nao-Sheng],
An Improved TV Model for Image Restoration,
CISP09(1-5).
IEEE DOI 0910
BibRef

Jiang, X.N.[Xue-Na], Liu, Y.Y.[Yang-Yang], Wang, S.J.[Shou-Jue],
A Novel Geometric Algorithm for Blind Image Restoration Based on High-Dimensional Space,
CISP09(1-5).
IEEE DOI 0910
BibRef

Fang, S.L.[Sheng-Liang], Zhang, X.W.[Xi-Wang],
A New Algorithm for Revising Noise Covariance Matrix Disturbance in the Presence of Coherent Sources,
CISP09(1-6).
IEEE DOI 0910
BibRef

An, Z.J.[Zhi-Juan],
Fast subspace estimation with Rayleigh-Ritz approximation and multi-stage Wiener filter,
IASP09(381-385).
IEEE DOI 0904
BibRef

Gao, Y.[Yan], Chan, K.L.[Kap Luk], Yau, W.Y.[Wei-Yun],
Manifold denoising with Gaussian Process Latent Variable Models,
ICPR08(1-4).
IEEE DOI 0812
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Tanaka, M.[Masayuki], Okutomi, M.[Masatoshi],
Locally adaptive learning for translation-variant MRF image priors,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Christiansen, O.,
Time-Frequency Analysis and its Applications in Denoising,
Ph.D.Thesis, 2002, Department of Informatics, University of Bergen, Norway. BibRef 0200

Miao, L.[Lidan], Qi, H.R.[Hai-Rong],
A Blind Source Separation Perspective on Image Restoration,
CVPR07(1-7).
IEEE DOI 0706
BibRef

Loog, M.[Marco],
Generic Blind Source Separation Using Second-Order Local Statistics,
SSPR06(844-852).
Springer DOI 0608
BibRef

Gerace, I.,
Solving the Sparse Data Image Restoration Problem by Local Minimization,
ICIP05(III: 321-324).
IEEE DOI 0512
BibRef

Wu, X.J.[Xian-Jin], Wang, R.S.[Run-Sheng], Wang, C.[Cheng],
Regularized image restoration based on adaptively selecting parameter and operator,
ICPR04(III: 662-665).
IEEE DOI 0409
BibRef

Gutierrez, J., Malo, J., Ferri, F.J.,
Perceptual regularization functionals for natural image restoration,
ICIP03(II: 989-992).
IEEE DOI 0312
BibRef

Ben Hamza, A., Krim, H.[Hamid],
Towards a Unified View of Estimation: Variational vs Statistical,
ICIP01(II: 577-580).
IEEE DOI 0108
BibRef

Devcic, Z., Loncaric, S.,
Blind Restoration of Space-invariant Image Degradations in the Singular Value Decomposition Domain,
ICIP01(II: 49-52).
IEEE DOI 0108
BibRef

Rao, K.D.[K. Deergha], Swamy, M.N.S., Plotkin, E.I.,
Image Restoration Using an Hybrid Approach Based on DWT and SMKF,
ICIP01(I: 249-252).
IEEE DOI 0108
BibRef

Fan, L.X.[Li-Xin], Sung, K.K.[Kah Kay],
Towards Statistical Image Restoration: Perceptual Grouping as Regularizing Operators,
PercOrg01(xx-yy). 0106
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Ishwar, P.[Prakash], Moulin, P.[Pierre],
Shift Invariant Restoration: An Overcomplete Maxent MAP Framework,
ICIP00(Vol III: 270-272).
IEEE DOI 0008
BibRef
Earlier: Ishwar, P.[Prakash], Moulin, P.[Pierre],
Multiple-Domain Image Modeling and Restoration,
ICIP99(I:362-366).
IEEE DOI BibRef

Haseyama, M., Takezawa, M., Kitajima, H.,
An Image Restoration Method Using IFS,
ICIP00(Vol III: 774-777).
IEEE DOI 0008
BibRef

Fridrich, J.[Jiri], Goljan, M.[Miroslav],
Images with Self-Correcting Capabilities,
ICIP99(III:792-796).
IEEE DOI BibRef 9900

Inoue, J.I.,
Image restoration using quantum fluctuation,
CIAP99(130-135).
IEEE DOI 9909
BibRef

Murli, A., d'Amore, L., de Simone, V.,
The Wiener filter and regularization methods for image restoration problems,
CIAP99(394-399).
IEEE DOI 9909
BibRef

Herzog, A.[Andreas], Krell, G.[Gerald], Michaelis, B.[Bernd], Wang, J.Z.[Ji-Zhong], Zuschratter, W.[Werner], Braun, K.[Katharina],
Three-dimensional quasi-binary image restoration for confocal microscopy and its application to dendritic trees,
CAIP97(114-121).
Springer DOI 9709
BibRef

Barakat, V., Guilpart, B., Goutte, R., and Prost, R.,
Model-Based Tikhonov-Miller Image Restoration,
ICIP97(I: 310-313).
IEEE DOI BibRef 9700

Suthaharan, S., and Zhang, Z.W.[Zhong-Wei],
SNR Optimization Using Genetic Algorithm,
ICIP97(I: 295-297).
IEEE DOI 9710
BibRef

Chen, Y.W.[Yen-Wei], Nakao, Z., Fang, X.[Xue], Tamura, S.,
A Parallel Genetic Algorithm for Image Restoration and Its Performance,
ICPR96(IV: 694-698).
IEEE DOI 9608
(Univ. of the Ryukyus, J) BibRef

Lasakul, A., Atsuta, K., Kondo, S.,
A Theory of Image Restoration for Linear Spatial Degradation Using Multiresolution Analysis,
ICPR96(II: 422-426).
IEEE DOI 9608
(Tokai Univ., J) BibRef

Ibrahim, H.M.[Hosny M.], Gharieb, R.R.,
Two-dimensional cumulant-based adaptive enhancer,
ICIP96(I: 789-792).
IEEE DOI BibRef 9600

Hirani, A.N.[Anil N.], Totsuka, T.[Takashi],
Dual domain interactive image restoration: basic algorithm,
ICIP96(I: 797-800).
IEEE DOI BibRef 9600

Cohen, H.A.[Harvey A.],
Image restoration via N-nearest neighbour classification,
ICIP96(I: 1005-1008).
IEEE DOI BibRef 9600

Koivunen, V.[Visa], Kassam, S.A.,
Covariance estimation in multivariate OS-filtering,
ICIP96(I: 981-984).
IEEE DOI BibRef 9600

Lau, D.L.[Daniel L.], Arce, G.R.[Gonzalo R.], Gallagher, Jr., N.C.[Neal C.],
Robust Image Wavelet Shrinkage for Denoising,
ICIP96(I: 371-374).
IEEE DOI BibRef 9600

Schonfeld, D., Qiao, Y.[Yi],
A new stochastic projection-based image recovery method,
ICIP95(I: 466-469).
IEEE DOI 9510
BibRef

Grigorian, A.[Artiom],
New models of image restoration,
CAIP95(509-514).
Springer DOI 9509
BibRef

Maitre, H.,
A General Scheme for Signal Restoration with Application to Picture Processing,
PRIP81(430-432). BibRef 8100

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Image Restoration -- General, Survey, Evaluations .