4.1.1 Inverse Problems

Chapter Contents (Back)
Inverse Problems.

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


Runkel, C.[Christina], Moeller, M.[Michael], Schönlieb, C.B.[Carola-Bibiane], Etmann, C.[Christian],
Learning Posterior Distributions in Underdetermined Inverse Problems,
SSVM23(187-209).
Springer DOI 2307
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 .


Last update:May 6, 2024 at 15:50:14