5.3.1 Image Restoration -- General, Survey, Evaluations

Chapter Contents (Back)
Restoration. Survey, Restoration.
See also Image Quality Evaluation, Visual Quality, Quality Assessment, and Imaging Models.
See also Non-Local Means for Denoising.

Vidal, R.G.[Rosaura G.], Banerjee, S.[Sreya], Grm, K.[Klemen], Struc, V.[Vitomir], Scheirer, W.J.[Walter J.],
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition,

WWW Link. 1901

Earlier: WACV18(1597-1606)
IEEE DOI 1806
Dataset, Image Restoration. Used for restoration challenges at CVPR. image classification, image enhancement, image restoration, learning (artificial intelligence), object detection, Visualization BibRef

Harris, J.L.,
Image Evaluation and Restoration,
JOSA(56), No. 5, May 1966, pp. 569-574. BibRef 6605

Rushforth, C.K., Harris, R.W.,
Restoration, Resolution, and Noise,
JOSA(58), No. 4, April 1968, pp. 539-545. BibRef 6804

Trussell, H.J., Civanlar, M.R.,
The Feasible Solution in Signal Restoration,
ASSP(32), No. 2, 1984, pp. 201-212. Constraints described as convex sets. BibRef 8400

Sezan, M.I., Tekalp, A.M., (Eds.)
Image Restoration and Reconstruction,
OptEng(29), No. 5, May 1990, pp. 391-574. Special issue. BibRef 9005

Sezan, M.I., Stark, H.,
Image restoration by the method of convex projections: Part 2,
MedImg(1), October 1982, pp. xx-yy. BibRef 8210

Sezan, M.I., Tekalp, A.M.,
Survey of recent developments in digital image restoration,
OptEng(29), No. 5, May 1990, pp. 393-404. Survey, Restoration. In the Special issue. BibRef 9005

Katsaggelos, A.K., Mammone, R.J., (Eds.)
Special Issue: Image Restoration,
JVCIR(3), No. 4, December 1992, pp. 305-455. BibRef 9212

Katsaggelos, A.K.,
Digital Image Restoration (Book),
SpringerBerlin, 1991. BibRef 9100

Banham, M.R., Katsaggelos, A.K.,
Digital Image Restoration,
SPMag(14), No. 2, March 1997, pp. 24-41. 9704
BibRef

Hong, M.C., Stathaki, T., and Katsaggelos, A.K.,
A Mixed Norm Image Restoration,
ICIP97(I: 385-388).
IEEE DOI BibRef 9700

May, K., Stathaki, T., Constantinides, A.G., Katsaggelos, A.K.,
Iterative determination of local bound constraints in iterative image restoration,
ICIP98(II: 833-837).
IEEE DOI 9810
BibRef

Hong, M.C.[Min-Cheol], Stathaki, T.,
A regularized least mean mixed norm multichannel image restoration algorithm,
ICIP98(II: 828-832).
IEEE DOI 9810
BibRef

Reeves, S.J., Mersereau, R.M.,
Automatic assessment of constraint sets in image restoration,
IP(1), No. 1, January 1992, pp. 119-123.
IEEE DOI 0402
BibRef

Reeves, S.J.,
Optimal space-varying regularization in iterative image restoration,
IP(3), No. 3, May 1994, pp. 319-324.
IEEE DOI 0402
BibRef

Reeves, S.J.,
Fast image restoration without boundary artifacts,
IP(14), No. 10, October 2005, pp. 1448-1453.
IEEE DOI 0510
BibRef

King, I.R.,
Some Practical Aspects of Image Restoration,
IJIST(6), No. 4, Winter 1995, pp. 392-394. BibRef 9500

Choy, S.S.O., Chan, Y.H.[Yuk-Hee], Siu, W.C.[Wan-Chi],
New Image Restoration Performance-Measures with High-Precision,
OptEng(36), No. 4, April 1997, pp. 1035-1043. 9705
BibRef
Earlier:
New adaptive iterative image restoration algorithm,
ICIP94(II: 670-674).
IEEE DOI 9411
BibRef

Fienup, J.R.,
Invariant Error Metrics for Image Reconstruction,
AppOpt(36), No. 32, November 10 1997, pp. 8352-8357. 9711
BibRef

Thurman, S.T.[Samuel T.], Fienup, J.R.[James R.],
Noise histogram regularization for iterative image reconstruction algorithms,
JOSA-A(24), No. 3, March 2007, pp. 608-617.
WWW Link. 0801
BibRef

Uma, S., Annadurai, S.,
A Review: Restoration Approaches,
GVIP(05), No. V8, 2005, pp. 23-35.
HTML Version. BibRef 0500

Hasegawa, Y.[Yasumichi],
Algebraically Approximate and Noisy Realization of Discrete-Time Systems and Digital Images,
Springer-Verlag2009. ISBN: 978-3-642-03216-5
WWW Link. Buy this book: Algebraically Approximate and Noisy Realization of Discrete-Time Systems and Digital Images (Lecture Notes in Electrical Engineering) 0910
BibRef

Chatterjee, P., Milanfar, P.[Peyman],
Is Denoising Dead?,
IP(19), No. 4, April 2010, pp. 895-911.
IEEE DOI 1003
Evaluation, Denoising. Since recent work seems to be about as good as the past, is there a theoretical limit and are we there yet? Conclusion, not at the limit yet. BibRef

Chatterjee, P., Milanfar, P.[Peyman],
Practical Bounds on Image Denoising: From Estimation to Information,
IP(20), No. 5, May 2011, pp. 1221-1233.
IEEE DOI 1104

See also Patch-Based Near-Optimal Image Denoising. BibRef

Russo, F.,
New Method for Performance Evaluation of Grayscale Image Denoising Filters,
SPLetters(17), No. 5, May 2010, pp. 417-420.
IEEE DOI 1003
BibRef

Chickerur, S.[Satyadhyan], Kumar M., A.[Aswatha],
Image Restoration: Past, Present and Future,
RPCS(3), No. 3 2010, pp. 108-126.
WWW Link. 1003
BibRef

Jouini, W.,
Energy Detection Limits Under Log-Normal Approximated Noise Uncertainty,
SPLetters(18), No. 7, July 2011, pp. 423-426.
IEEE DOI 1101
BibRef

Shao, L., Yan, R., Li, X., Liu, Y.,
From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms,
Cyber(44), No. 7, July 2014, pp. 1001-1013.
IEEE DOI 1407
Survey, Denoising. Dictionaries BibRef

Zhang, L., Zuo, W.,
Image Restoration: From Sparse and Low-Rank Priors to Deep Priors,
SPMag(34), No. 5, September 2017, pp. 172-179.
IEEE DOI 1709
Lecture Notes. Cameras, Digital cameras, Digital imaging, Dynamic range, Encoding, Machine learning, Transform, coding BibRef

Rasti, B.[Behnood], Scheunders, P.[Paul], Ghamisi, P.[Pedram], Licciardi, G.[Giorgio], Chanussot, J.[Jocelyn],
Noise Reduction in Hyperspectral Imagery: Overview and Application,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Wu, W.C.[Wen-Cong], Chen, M.F.[Ming-Fei], Xiang, Y.[Yu], Zhang, Y.G.[Yun-Gang], Yang, Y.[Yang],
Recent progress in image denoising: A training strategy perspective,
IET-IPR(17), No. 6, 2023, pp. 1627-1657.
DOI Link 2305
deep neural networks, image denoising, image processing, image restoration BibRef

Kui, M.Y.[Meng-Yun], Xu, Y.[Yunna], Wang, J.L.[Jin-Liang], Cheng, F.[Feng],
Research on the Adaptability of Typical Denoising Algorithms Based on ICESat-2 Data,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
BibRef

Li, Y.[Yawei], Zhang, Y.[Yulun], Timofte, R.[Radu], Van Gool, L.J.[Luc J.], Tu, Z.J.[Zhi-Jun], Du, K.[Kunpeng], Wang, H.[Hailing], Chen, H.[Hanting], Li, W.[Wei], Wang, X.F.[Xiao-Fei], Hu, J.[Jie], Wang, Y.H.[Yun-He], Kong, X.Y.[Xiang-Yu], Wu, J.L.[Jin-Long], Zhang, D.[Dafeng], Zhang, J.X.[Jian-Xing], Liu, S.[Shuai], Bai, F.[Furui], Feng, C.[Chaoyu], Wang, H.[Hao], Zhang, Y.Q.[Yu-Qian], Shao, G.Q.[Guang-Qi], Wang, X.T.[Xiao-Tao], Lei, L.[Lei], Xu, R.J.[Rong-Jian], Zhang, Z.[Zhilu], Chen, Y.J.[Yun-Jin], Ren, D.W.[Dong-Wei], Zuo, W.M.[Wang-Meng], Wu, Q.[Qi], Han, M.Y.[Ming-Yan], Cheng, S.[Shen], Li, H.P.[Hai-Peng], Jiang, T.[Ting], Jiang, C.Z.[Cheng-Zhi], Li, X.P.[Xin-Peng], Luo, J.[Jinting], Lin, W.J.[Wen-Jie], Yu, L.[Lei], Fan, H.Q.[Hao-Qiang], Liu, S.C.[Shuai-Cheng], Arora, A.[Aditya], Zamir, S.W.[Syed Waqas], Vazquez-Corral, J.[Javier], Derpanis, K.G.[Konstantinos G.], Brown, M.S.[Michael S.], Li, H.[Hao], Zhao, Z.H.[Zhi-Hao], Pan, J.S.[Jin-Shan], Dong, J.X.[Jiang-Xin], Tang, J.H.[Jin-Hui], Yang, B.[Bo], Chen, J.X.[Jing-Xiang], Li, C.H.[Cheng-Hua], Zhang, X.[Xi], Zhang, Z.[Zhao], Ren, J.H.[Jia-Huan], Ji, Z.C.[Zhi-Cheng], Miao, K.[Kang], Zhao, S.[Suiyi], Zheng, H.[Huan], Wei, Y.[YanYan], Liu, K.[Kangliang], Du, X.C.[Xiang-Cheng], Liu, S.[Sijie], Zheng, Y.B.[Ying-Bin], Wu, X.J.[Xing-Jiao], Jin, C.[Cheng], Irny, R.[Rajeev], Koundinya, S.[Sriharsha], Kamath, V.[Vighnesh], Khandelwal, G.[Gaurav], Khowaja, S.A.[Sunder Ali], Yoon, J.[Jiseok], Lee, I.H.[Ik Hyun], Chen, S.J.[Shi-Jie], Zhao, C.Q.[Cheng-Qiang], Yang, H.[Huabin], Zhang, Z.J.[Zhong-Jian], Huang, J.[Junjia], Zhang, Y.[Yanru],
NTIRE 2023 Challenge on Image Denoising: Methods and Results,
NTIRE23(1905-1921)
IEEE DOI 2309
BibRef


Albert, P.[Paul], Arazo, E.[Eric], Krishna, T.[Tarun], O'Connor, N.E.[Noel E.], McGuinness, K.[Kevin],
Is your noise correction noisy? PLS: Robustness to label noise with two stage detection,
WACV23(118-127)
IEEE DOI 2302
Training, Semantics, Neural networks, Benchmark testing, Robustness, Noise robustness, Algorithms: Machine learning architectures, visual reasoning BibRef

Paulino, I.R.[Ignacio Ramírez],
Practical Bulk Denoising Of Large Binary Images,
ICIP22(196-200)
IEEE DOI 2211
Noise reduction, Benchmark testing, image denoising, binary image denoising, DUDE, non-local means, real noise BibRef

Chen, L.[Liangyu], Chu, X.J.[Xiao-Jie], Zhang, X.Y.[Xiang-Yu], Sun, J.[Jian],
Simple Baselines for Image Restoration,
ECCV22(VII:17-33).
Springer DOI 2211
BibRef

Abdelhamed, A.[Abdelrahman], Afifi, M.[Mahmoud], Timofte, R.[Radu], Brown, M.S.[Michael S.], Cao, Y.[Yue], Zhang, Z.L.[Zhi-Lu], Zuo, W.M.[Wang-Meng], Zhang, X.L.[Xiao-Ling], Liu, J.[Jiye], Chen, W.D.[Wen-Dong], Wen, C.Y.[Chang-Yuan], Liu, M.[Meng], Lv, S.L.[Shuai-Lin], Zhang, Y.C.[Yun-Chao], Pan, Z.H.[Zhi-Hong], Li, B.[Baopu], Xi, T.[Teng], Fan, Y.W.[Yan-Wen], Yu, X.Y.[Xi-Yu], Zhang, G.[Gang], Liu, J.T.[Jing-Tuo], Han, J.Y.[Jun-Yu], Ding, E.[Errui], Yu, S.H.[Song-Hyun], Park, B.J.[Bum-Jun], Jeong, J.C.[Je-Chang], Liu, S.[Shuai], Zong, Z.Y.[Zi-Yao], Nan, N.[Nan], Li, C.H.[Cheng-Hua], Yang, Z.L.[Zeng-Li], Bao, L.[Long], Wang, S.Q.[Shuang-Quan], Bai, D.W.[Dong-Woon], Lee, J.W.[Jung-Won], Kim, Y.J.[Young-Jung], Rho, K.[Kyeongha], Shin, C.Y.[Chang-Yeop], Kim, S.H.[Sung-Ho], Tang, P.L.[Peng-Liang], Zhao, Y.Y.[Yi-Yun], Zhou, Y.Q.[Yu-Qian], Fan, Y.C.[Yu-Chen], Huang, T.S.[Thomas S.], Li, Z.H.[Zhi-Hao], Shah, N.A.[Nisarg A.], Liu, W.[Wei], Yan, Q.[Qiong], Zhao, Y.Z.[Yu-Zhi], Mozejko, M.[Marcin], Latkowski, T.[Tomasz], Treszczotko, L.[Lukasz], Szafraniuk, M.[Michal], Trojanowski, K.[Krzysztof], Wu, Y.H.[Yan-Hong], Michelini, P.N.[Pablo Navarrete], Hu, F.S.[Feng-Shuo], Lu, Y.H.[Yun-Hua], Kim, S.J.[Su-Jin], Kim, W.J.[Won-Jin], Lee, J.[Jaayeon], Choi, J.H.[Jang-Hwan], Zhussip, M.[Magauiya], Khassenov, A.[Azamat], Kim, J.H.[Jong Hyun], Cho, H.[Hwechul], Kansal, P.[Priya], Nathan, S.[Sabari], Ye, Z.Y.[Zhang-Yu], Lu, X.W.[Xi-Wen], Wu, Y.Q.[Ya-Qi], Yang, J.X.[Jiang-Xin], Cao, Y.L.[Yan-Long], Tang, S.L.[Si-Liang], Cao, Y.P.[Yan-Peng], Maggioni, M.[Matteo], Marras, I.[Ioannis], Tanay, T.[Thomas], Slabaugh, G.[Gregory], Yan, Y.L.[You-Liang], Kang, M.J.[Myung-Joo], Choi, H.S.[Han-Soo], Song, K.M.[Kyung-Min], Xu, S.S.[Shu-Song], Lu, X.M.[Xiao-Mu], Wang, T.N.[Ting-Niao], Lei, C.X.[Chun-Xia], Liu, B.[Bin], Gupta, R.[Rajat], Kumar, V.[Vineet],
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results,
NTIRE20(2077-2088)
IEEE DOI 2008
Image color analysis, Image denoising, Noise measurement, Testing, Noise reduction, Runtime, Pipelines BibRef

Plötz, T., Roth, S.,
Benchmarking Denoising Algorithms with Real Photographs,
CVPR17(2750-2759)
IEEE DOI 1711
Benchmark testing, Cameras, Gaussian noise, ISO, Noise measurement, Noise reduction, Protocols BibRef

Akkoul, S.[Smaïl], Ledee, R.[Roger], Leconge, R.[Remy], Leger, C.[Christophe], Harba, R.[Rachid], Pesnel, S.[Sabrina], Lerondel, S.[Stéphanie], Lepape, A.[Alain], Vilcahuaman, L.[Luis],
Comparison of Image Restoration Methods for Bioluminescence Imaging,
ICISP08(163-172).
Springer DOI 0807
BibRef

Kokaram, A.[Anil],
Ten Years of Digital Visual Restoration Systems,
ICIP07(IV: 1-4).
IEEE DOI 0709
BibRef

Kornprobst, P., Deriche, R., Gilles, A.,
Nonlinear Operators in Image Restoration,
CVPR97(325-330).
IEEE DOI 9704
Halfquadratic minimization is best.
PS File. BibRef

Ferreira, P.J.S.G.,
The stability of certain image restoration problems: Quantitative results,
ICIP95(II: 29-32).
IEEE DOI 9510
BibRef

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Image Restoration: Filter Approaches .


Last update:Mar 16, 2024 at 20:36:19