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All issues Volume 28 / No 1 (February 2023) Wuhan Univ. J. Nat. Sci., 28 1 (2023) 53-60 References
Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 28, Number 1, February 2023
Page(s) 53 - 60
DOI https://doi.org/10.1051/wujns/2023281053
Published online 17 March 2023
  1. Blomgren P, Chan T F. Color TV: Total variation methods for restoration of vector-valued images[J]. IEEE Transactions on Image Processing, 1998, 7(3):304-309. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  2. Bertalmio M, Caselles V, Rouge B, et al. A TV based restoration model with local constraints. [J]. Journal of Scientific Computing, 2003, 19(1):95-122. [Google Scholar]
  3. Li H, Liu F. Image denoising via sparse and redundant representations over learned dictionaries in wavelet domain[C]// 2009 Fifth International Conference on Image and Graphics. Washington D C: IEEE, 2009: 754-758. [Google Scholar]
  4. Mairal J, Elad M, Sapiro G. Sparse representation for color image restoration[J]. IEEE Transactions on Image Processing, 2007, 17(1):53-69. [Google Scholar]
  5. Aharon M, Elad M, Bruckstein A M. On the uniqueness of overcomplete dictionaries, and a practical way to retrieve them[J]. Linear Algebra & Its Applications, 2006, 416(1):48-67. [CrossRef] [MathSciNet] [Google Scholar]
  6. Zha Z, Yuan X, Zhu C, et al. Image restoration via simultaneous nonlocal selfsimilarity priors[J]. IEEE Transactions on Image Processing, 2020, 29: 8561-8576. [NASA ADS] [CrossRef] [Google Scholar]
  7. Gu S H, Zhang L, Zuo W M, et al. Weighted nuclear norm minimization with application to image denoising[C]// 2014 IEEE Conference on Computer Vision and Pattern Recognition. Washington D C : IEEE, 2014: 2862-2869. [Google Scholar]
  8. Zhang K, Zuo W M, Chen Y J, et al. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 2016, 26(7):3142-3155. [Google Scholar]
  9. Dong W S, Wang P Y, Yin W T, et al. Denoising prior driven deep neural network for image restoration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(10): 2305-2318. [CrossRef] [PubMed] [Google Scholar]
  10. Zhang K, Zuo W M, Gu S H, et al. Learning deep CNN denoiser prior for image restoration[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Washington D C : IEEE, 2017: 2808-2817. [Google Scholar]
  11. Aharon M, Elad M, Bruckstein. A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322. [CrossRef] [Google Scholar]
  12. Mairal J, Bach F, Ponce J, et al. Non-local sparse models for image restoration[C]// 2009 IEEE 12th International Conference on Computer Vision. Washington D C : IEEE, 2009: 2272-2279 [Google Scholar]
  13. Dong W S, Zhang L, Shi G M, et al. Nonlocally centralized sparse representation for image restoration[J]. IEEE Transcation on Image Processing, 2013, 22(4):1620-1630. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  14. Zhang J, Zhao D B, Gao W. Group-based sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2014, 23(8): 3336-3351. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  15. Zha Z Y, Yuan X, Wen B H, et al. Group sparsity residual constraint with non-local priors for image restoration[J]. IEEE Transactions on Image Processing, 2020, 29: 8960-8975. [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  16. Bai M, Zhang X, Shao Q. Adaptive correction procedure for TVL1 image deblurring under impulse noise[J]. Inverse Problems, 2016, 32(8): 289-338. [Google Scholar]
  17. Rahimi Y, Wang C, Dong H B, et al. A scale-invariant approach for sparse signal recovery[J]. SIAM Journal on Scientific Computing, 2019, 41(6):A3649-A3672. [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  18. Xu Z B, Chang X Y, Xu F M, et al. L-1/2 regularization: A thresholding representation theory and a fast solver[J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(7):1013-1027. [CrossRef] [PubMed] [Google Scholar]
  19. Zuo W M, Meng D Y, Zhang L, et al. A generalized iterated shrinkage algorithm for non-convex sparse coding[C]// 2013 IEEE International Conference on Computer Vision. Washington D C: IEEE, 2013: 217-224. [Google Scholar]
  20. Jia X, Feng X. Bayesian inference for adaptive low rank and sparse matrix estimation[J]. Neurocomputing, 2018(5):71-83. [CrossRef] [Google Scholar]
  21. Keller J M, Gray M R, Givens J A. A fuzzy k-nearest neighbor algorithm[J]. IEEE Transactions on Systems Man & Cybernetics,1985, SMC-15(4): 580-585. [CrossRef] [Google Scholar]
  22. Dong W S, Lei Z, Shi G M, et al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization[J]. IEEE Transactions on Image Processing, 2011, 20(7):1838-1857. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  23. Buades A, Coll B, Morel J M. A non-local algorithm for image denoising[C]// 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). Washington D C: IEEE, 2005, 2: 60-65. [Google Scholar]
  24. Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 2007, 16(8):2080-2095. [CrossRef] [Google Scholar]
  25. Xu J, Zhang L , Zuo W M, et al. Patch group based nonlocal self-similarity prior learning for image denoising[C]// 2015 IEEE International Conference on Computer Vision (ICCV). Washington D C: IEEE, 2016: 244-252. [Google Scholar]
  26. Dong W S, Shi G M, Li X. Nonlocal image restoration with bilateral variance estimation: A low-rank approach[J]. IEEE Transactions on Image Processing, 2013, 22(2):700-711. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]

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