Terzopoulos, D.[Demetri],
Regularization of Inverse Visual Problems Involving Discontinuities,
PAMI(8), No. 4, July 1986, pp. 413-424.
A proposal of stabilizing functions for use in inverse vision
problems. There are a lot of references, and this may really go
with his relaxation papers.
BibRef
8607
Terzopoulos, D.[Demetri],
Visual Modelling,
BMVC91(xx-yy).
PDF File.
9109
BibRef
Terzopoulos, D.[Demetri],
Controlled-Smoothness Stabilizers fo the Regularization of
Ill-Posed Visual Problems Involving Discontinuities,
DARPA84(225-229).
BibRef
8400
Marroquin, J.L.[Jose L.],
Velasco, F.A.[Fernando A.],
Rivera, M.[Mariano], and
Nakamura, M.[Miguel],
Gauss-Markov Measure Field Models for Low-Level Vision,
PAMI(23), No. 4, April 2001, pp. 337-348.
IEEE DOI
0104
Model using Bayesian Estimation Theory with prior MRF models. Applied to
segmentation, texture directions, classification, quantization.
BibRef
Marroquin, J.L.,
Mitter, S.K., and
Poggio, T.A.,
Probabilistic Solution of Ill-Posed Problems in Computational Vision,
ASAJ(82), No. 397, March 1987, pp. 76-89.
BibRef
8703
Earlier:
DARPA85(293-309).
BibRef
And:
MIT AI Memo-97, March 1987.
BibRef
Marroquin, J.L.,
Deterministic Bayesian Estimation of Markovian Random Fields with
Applications to Computational Vision,
ICCV87(597-601).
BibRef
8700
Marroquin, J.L.[Jose Luis],
Probabilistic Solution of Inverse Problems,
MIT AI-TR-860, September 1985.
BibRef
8509
Ph.D.Thesis. 1985.
WWW Link.
BibRef
Shulman, D.,
Regularization of Inverse Problems in Low-Level
Vision While Preserving Discontinuities,
Ph.D.Thesis (CS), Univ. of Maryland, August 1990.
How to deal with edges in a regularization function.
BibRef
9008
Stevenson, R.L.,
Schmitz, B.E.,
Delp, E.J.,
Discontinuity Preserving Regularization of Inverse Visual Problems,
SMC(24), No. 3, March 1994, pp. 455-469.
BibRef
9403
de Micheli, E.[Enrico],
Viano, G.A.[Giovanni Alberto],
Probabilistic regularization in inverse optical imaging,
JOSA-A(17), No. 11, November 2000, pp. 1942-1951.
0011
BibRef
de Micheli, E.[Enrico],
Viano, G.A.[Giovanni Alberto],
Inverse optical imaging viewed as a backward channel communication
problem,
JOSA-A(26), No. 6, June 2009, pp. 1393-1402.
WWW Link.
0906
BibRef
Xu, J.,
Osher, S.J.[Stanley J.],
Iterative Regularization and Nonlinear Inverse Scale Space Applied to
Wavelet-Based Denoising,
IP(16), No. 2, February 2007, pp. 534-544.
IEEE DOI
0702
BibRef
Bioucas-Dias, J.M.[Jose M.],
Figueiredo, M.A.T.[Mario A.T.],
A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for
Image Restoration,
IP(16), No. 12, December 2007, pp. 2992-3004.
IEEE DOI
0711
BibRef
Earlier:
Two-Step Algorithms for Linear Inverse Problems with Non-Quadratic
Regularization,
ICIP07(I: 105-108).
IEEE DOI
0709
BibRef
Afonso, M.V.[Manya V.],
Bioucas-Dias, J.M.[Jose M.],
Figueiredo, M.A.T.[Mario A. T.],
An augmented Lagrangian approach to linear inverse problems with
compound regularization,
ICIP10(4169-4172).
IEEE DOI
1009
BibRef
Bioucas-Dias, J.M.[Jose M.],
Figueiredo, M.A.T.[Mario A.T.],
An iterative algorithm for linear inverse problems with compound
regularizers,
ICIP08(685-688).
IEEE DOI
0810
BibRef
Beck, A.[Amir],
Teboulle, M.[Marc],
A Fast Iterative Shrinkage-Thresholding Algorithm For Linear
Inverse Problems,
SIIMS(2), No. 1, 2009, pp. 183-202.
iterative shrinkage-thresholding algorithm, deconvolution, linear
inverse problem, least squares and L_1 regularization problems;
optimal gradient method, global rate of convergence, two-step
iterative algorithms, image deblurring
DOI Link
BibRef
0900
Alexeev, B.,
Ward, R.,
On the Complexity of Mumford-Shah-Type Regularization, Viewed as a
Relaxed Sparsity Constraint,
IP(19), No. 10, October 2010, pp. 2787-2789.
IEEE DOI
1003
Inverse problems are NP-hard in general unlike Mumford-Shah functional, thus
it can't solve them exactly.
BibRef
Deledalle, C.A.[Charles-Alban],
Vaiter, S.[Samuel],
Fadili, J.[Jalal],
Peyré, G.[Gabriel],
Stein Unbiased GrAdient estimator of the Risk (SUGAR) for Multiple
Parameter Selection,
SIIMS(7), No. 4, 2014, pp. 2448-2487.
DOI Link
1412
solving variational regularization of ill-posed inverse problems
BibRef
Zhu, T.[Tao],
New over-relaxed monotone fast iterative shrinkage-thresholding
algorithm for linear inverse problems,
IET-IPR(13), No. 14, 12 December 2019, pp. 2888-2896.
DOI Link
1912
BibRef
Zhu, T.[Tao],
Accelerating monotone fast iterative shrinkage-thresholding algorithm
with sequential subspace optimization for sparse recovery,
SIViP(14), No. 4, June 2020, pp. 771-780.
WWW Link.
2005
BibRef
Liu, R.S.[Ri-Sheng],
Cheng, S.C.[Shi-Chao],
He, Y.[Yi],
Fan, X.[Xin],
Lin, Z.C.[Zhou-Chen],
Luo, Z.X.[Zhong-Xuan],
On the Convergence of Learning-Based Iterative Methods for Nonconvex
Inverse Problems,
PAMI(42), No. 12, December 2020, pp. 3027-3039.
IEEE DOI
2011
Inverse problems, Convergence, Iterative methods, Learning systems,
Acceleration, Iterative algorithms, Learning systems,
rain streaks removal
BibRef
Hong, B.,
Koo, J.,
Burger, M.,
Soatto, S.,
Adaptive Regularization of Some Inverse Problems in Image Analysis,
IP(29), 2020, pp. 2507-2521.
IEEE DOI
2001
Adaptive regularization, Huber-Huber model, convex optimization,
ADMM, segmentation, optical flow, denoising
BibRef
Schwab, J.[Johannes],
Antholzer, S.[Stephan],
Haltmeier, M.[Markus],
Big in Japan: Regularizing Networks for Solving Inverse Problems,
JMIV(62), No. 3, April 2020, pp. 445-455.
Springer DOI
2004
Introduce and rigorously analyze families of deep regularizing neural networks.
BibRef
Ebner, A.[Andrea],
Haltmeier, M.[Markus],
Plug-and-Play Image Reconstruction Is a Convergent Regularization
Method,
IP(33), 2024, pp. 1476-1486.
IEEE DOI
2402
Image reconstruction, Convergence, Stability criteria, Noise level,
Standards, Noise measurement, Inverse problems,
forward backward splitting
BibRef
Dittmer, S.[Sören],
Kluth, T.[Tobias],
Maass, P.[Peter],
Baguer, D.O.[Daniel Otero],
Regularization by Architecture: A Deep Prior Approach for Inverse
Problems,
JMIV(62), No. 3, April 2020, pp. 456-470.
Springer DOI
2004
BibRef
Colbrook, M.J.[Matthew J.],
WARPd: A Linearly Convergent First-Order Primal-Dual Algorithm for
Inverse Problems with Approximate Sharpness Conditions,
SIIMS(15), No. 3, 2022, pp. 1539-1575.
DOI Link
2209
BibRef
Klibanov, M.V.[Michael V.],
Li, J.Z.[Jing-Zhi],
Nguyen, L.H.[Loc H.],
Yang, Z.P.[Zhi-Peng],
Convexification Numerical Method for a Coefficient Inverse Problem
for the Radiative Transport Equation,
SIIMS(16), No. 1, 2023, pp. 35-63.
DOI Link
2301
BibRef
Klibanov, M.V.[Michael V.],
Li, J.Z.[Jing-Zhi],
Nguyen, L.H.[Loc H.],
Romanov, V.[Vladimir],
Yang, Z.P.[Zhi-Peng],
Convexification Numerical Method for a Coefficient Inverse Problem
for the Riemannian Radiative Transfer Equation,
SIIMS(16), No. 3, 2023, pp. 1762-1790.
DOI Link
2312
BibRef
Kobler, E.[Erich],
Effland, A.[Alexander],
Kunisch, K.[Karl],
Pock, T.[Thomas],
Total Deep Variation:
A Stable Regularization Method for Inverse Problems,
PAMI(44), No. 12, December 2022, pp. 9163-9180.
IEEE DOI
2212
Inverse problems, Optimal control, Training, Noise reduction,
Task analysis, Stability analysis, Trajectory,
variational methods
BibRef
Zhao, M.[Min],
Dobigeon, N.[Nicolas],
Chen, J.[Jie],
Guided Deep Generative Model-Based Spatial Regularization for
Multiband Imaging Inverse Problems,
IP(32), 2023, pp. 5692-5704.
IEEE DOI
2310
BibRef
Wang, J.L.,
Huang, T.Z.,
Zhao, X.L.,
Jiang, T.X.,
Ng, M.K.,
Multi-Dimensional Visual Data Completion via Low-Rank Tensor
Representation Under Coupled Transform,
IP(30), 2021, pp. 3581-3596.
IEEE DOI
2103
Tensors, Transforms, Correlation, Visualization, Color,
Discrete Fourier transforms, Videos, 2D framelet transform,
tensor completion
BibRef
Cheng, M.,
Jing, L.,
Ng, M.K.,
Tensor-Based Low-Dimensional Representation Learning for Multi-View
Clustering,
IP(28), No. 5, May 2019, pp. 2399-2414.
IEEE DOI
1903
learning (artificial intelligence), matrix decomposition,
pattern clustering, tensors,
tensor decomposition
BibRef
Luo, Y.[Yisi],
Zhao, X.L.[Xi-Le],
Li, Z.M.[Zhe-Min],
Ng, M.K.[Michael K.],
Meng, D.Y.[De-Yu],
Low-Rank Tensor Function Representation for Multi-Dimensional Data
Recovery,
PAMI(46), No. 5, May 2024, pp. 3351-3369.
IEEE DOI
2404
Tensors, Data models, Signal to noise ratio,
Point cloud compression, Matrix decomposition, Videos,
tensor factorization
BibRef
Luo, Y.[Yisi],
Zhao, X.[Xile],
Meng, D.Y.[De-Yu],
Jiang, T.X.[Tai-Xiang],
HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse
Problems in Multi-Dimensional Imaging,
CVPR22(19281-19290)
IEEE DOI
2210
Representation learning, Tensors, Inverse problems,
Noise reduction, Neural networks, Imaging, Transforms,
Self- semi- meta- unsupervised learning
BibRef
Hertrich, J.[Johannes],
Proximal Residual Flows for Bayesian Inverse Problems,
SSVM23(210-222).
Springer DOI
2307
BibRef
Laville, B.[Bastien],
Blanc-Féraud, L.[Laure],
Aubert, G.[Gilles],
Off-the-grid Charge Algorithm for Curve Reconstruction in Inverse
Problems,
SSVM23(393-405).
Springer DOI
2307
BibRef
Bianchi, D.[Davide],
Donatelli, M.[Marco],
Evangelista, D.[Davide],
Li, W.B.[Wen-Bin],
Piccolomini, E.L.[Elena Loli],
Graph Laplacian and Neural Networks for Inverse Problems in Imaging:
GraphLaNet,
SSVM23(175-186).
Springer DOI
2307
BibRef
Hu, Y.Y.[Yu-Yang],
Liu, J.M.[Jia-Ming],
Xu, X.J.[Xiao-Jian],
Kamilov, U.S.[Ulugbek S.],
Monotonically Convergent Regularization by Denoising,
ICIP22(426-430)
IEEE DOI
2211
Inverse problems, Noise reduction, Neural networks, Imaging,
Search problems, Stability analysis, Iterative methods,
model-based deep learning
BibRef
Aggrawal, H.O.[Hari Om],
Modersitzki, J.[Jan],
Hessian Initialization Strategies for L-BFGS Solving Non-linear Inverse
Problems,
SSVM21(216-228).
Springer DOI
2106
BibRef
Oberlin, T.[Thomas],
Verm, M.[Mathieu],
Regularization via Deep Generative Models: An Analysis Point of View,
ICIP21(404-408)
IEEE DOI
2201
Deep learning, Analytical models, Inverse problems,
Image processing, Superresolution, Neural networks, Estimation,
data-driven priors
BibRef
Chung, J.[Julianne],
Chung, M.[Matthias],
Slagel, J.T.[J. Tanner],
Iterative Sampled Methods for Massive and Separable Nonlinear Inverse
Problems,
SSVM19(119-130).
Springer DOI
1909
BibRef
Vidal, A.F.,
Pereyra, M.,
Maximum Likelihood Estimation of Regularisation Parameters,
ICIP18(1742-1746)
IEEE DOI
1809
Bayes methods, Kernel, Inverse problems, Imaging, Estimation, Sugar,
Markov processes, Image processing, inverse problems,
proximal algorithms
BibRef
Tuysuzoglu, A.[Ahmet],
Stojanovic, I.[Ivana],
Castanon, D.[David],
Karl, W.C.[W. Clem],
A graph cut method for linear inverse problems,
ICIP11(1913-1916).
IEEE DOI
1201
BibRef
Oraintara, S.,
Karl, W.C.,
Castanon, D.A.,
Nguyen, T.,
A Method for Choosing the Regularization Parameter in Generalized
Tikhonov Regularized Linear Inverse Problems,
ICIP00(Vol I: 93-96).
IEEE DOI
0008
BibRef
Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Connectionist Approaches to Computer Vision .