EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 29 / No 3 (June 2024) Wuhan Univ. J. Nat. Sci., 29 3 (2024) 219-227 References
Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 29, Number 3, June 2024
Page(s) 219 - 227
DOI https://doi.org/10.1051/wujns/2024293219
Published online 03 July 2024
  1. Richardson W H. Bayesian-based iterative method of image restoration[J]. Journal of the Optical Society of America, 1972, 62(1): 55-59. [NASA ADS] [CrossRef] [Google Scholar]
  2. Lucy L B. An iterative technique for the rectification of observed distributions[J]. The Astronomical Journal, 1974, 79(6): 745. [NASA ADS] [CrossRef] [Google Scholar]
  3. Wiener N. Extrapolation, Interpolation, and Smoothing of Stationary Time Series, with Engineering Applications[M]. Piscataqay: IEEE Xplore, 1949. [Google Scholar]
  4. Neelamani B R, Choi H, Baraniuk R. Wavelet-based deconvolution for ill-conditioned systems[C]//1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. New York: IEEE, 2002, 6: 3241-3244. [Google Scholar]
  5. Li J Z, Luisier F, Blu T. PURE-LET image deconvolution[J]. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 2018, 27(1): 92-105. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  6. Cho S, Lee S. Fast motion deblurring[C]//ACM SIGGRAPH Asia 2009 Papers. New York: ACM, 2009, 145: 1-8. [Google Scholar]
  7. Cai J F, Osher S, Shen Z W. Linearized bregman iterations for frame-based image deblurring[J]. SIAM Journal on Imaging Sciences, 2009, 2(1): 226-252. [CrossRef] [MathSciNet] [Google Scholar]
  8. Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D Nonlinear Phenomena, 1992, 60(1/2/3/4): 259-268. [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  9. Tao M, Yang J F. Alternating direction algorithms for total variation deconvolution in image reconstruction[EB/OL]. [2009-05-09]. https://legacy.sites.fas.harvard.edu/~cs278/papers/adm.pdf. [Google Scholar]
  10. Jiao Y L, Jin Q N, Lu X L, et al. Alternating direction method of multipliers for linear inverse problems[J]. SIAM Journal on Numerical Analysis, 2016, 54(4): 2114-2137. [Google Scholar]
  11. Wu C L, Tai X C. Augmented Lagrangian method, dual methods, and split bregman iteration for ROF, vectorial TV, and high order models[J]. SIAM Journal on Imaging Sciences, 2010, 3(3): 300-339. [CrossRef] [MathSciNet] [Google Scholar]
  12. Wu C L, Zhang J Y, Tai X C. Augmented Lagrangian method for total variation restoration with non-quadratic fidelity[J]. Inverse Problems & Imaging, 2011, 5(1): 237-261. [CrossRef] [MathSciNet] [Google Scholar]
  13. Wang Y L, Yang J F, Yin W T, et al. A new alternating minimization algorithm for total variation image reconstruction [J]. SIAM Journal on Imaging Sciences, 2008, 1(3): 248-272. [CrossRef] [MathSciNet] [Google Scholar]
  14. Ullah E, Nawaz R, Iqbal J. Single image haze removal using improved dark channel prior[C]//2013 5th International Conference on Modelling, Identification and Control (ICMIC). New York: IEEE, 2013: 245-248. [Google Scholar]
  15. Shi L, Ynag L, Chu S B, et al. Image haze removal using dark channel prior and minimizing energy function[C]//2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). New York: IEEE, 2017: 256-259. [CrossRef] [Google Scholar]
  16. Zhou K, Zhuang P X, Xiong J Y, et al. Blind image deblurring with joint extreme channels and L0-regularized intensity and gradient priors[C]//2020 IEEE International Conference on Image Processing (ICIP). New York: IEEE, 2020: 873-877. [CrossRef] [Google Scholar]
  17. Anwar S, Huynh C P, Porikli F. Image deblurring with a class-specific prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(9): 2112-2130. [CrossRef] [PubMed] [Google Scholar]
  18. Hosseini M S, Plataniotis K N. Convolutional deblurring for natural imaging[J]. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 2020, 29: 250-264. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  19. Chan R H, Riemenschneider S D, Shen L X, et al. Tight frame: An efficient way for high-resolution image reconstruction[J]. Applied and Computational Harmonic Analysis, 2004, 17(1): 91-115. [Google Scholar]
  20. Chan R H, Riemenschneider S D, Shen L X, et al. High-resolution image reconstruction with displacement errors: A framelet approach [J]. International Journal of Imaging Systems and Technology, 2004, 14(3): 91-104. [CrossRef] [Google Scholar]
  21. Chai A W, Shen Z W. Deconvolution: A wavelet frame approach[J]. Numerische Mathematik, 2007, 106(4): 529-587. [Google Scholar]
  22. Ono S, Miyata T, Yamada I. Cartoon-texture image decomposition using blockwise low-rank texture characterization [J] IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 2014, 23(3): 1128-1142. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  23. Tikhonov A. Regularization of incorrectly posed problems [J]. Soviet Math Dokl, 1963: 1624-1627. [Google Scholar]
  24. Gabay D. Chapter IX applications of the method of multipliers to variational inequalities[C]//Augmented Lagrangian Methods: Applications to the Numerical Solution of Boundary-Value Problems. Amsterdam: Elsevier, 1983, 15: 299-331. [CrossRef] [Google Scholar]
  25. Eckstein J, Bertsekas D P. On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators [J]. Mathematical Programming, 1992, 55(1): 293-318. [CrossRef] [MathSciNet] [Google Scholar]
  26. Lv X G, Huang T Z, Xu Z B, et al. Kronecker product approximations for image restoration with whole-sample symmetric boundary conditions[J]. Information Sciences, 2012, 186(1): 150-163. [CrossRef] [MathSciNet] [Google Scholar]
  27. Fazel M, Pong T K, Sun D F, et al. Hankel matrix rank minimization with applications to system identification and realization[J]. SIAM Journal on Matrix Analysis and Applications, 2013, 34(3): 946-977. [CrossRef] [MathSciNet] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.