Open Access
Wuhan Univ. J. Nat. Sci.
Volume 26, Number 6, December 2021
Page(s) 495 - 506
Published online 17 December 2021
  1. Abbass M Y, Kim H W, Abdelwahab S A, et al. Image deconvolution using homomorphic technique [J]. Signal, Image and Video Processing, 2019, 13(4): 703-709. [Google Scholar]
  2. Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms [J]. Physica D: Nonlinear Phenomena, 1992, 60(1-4): 259-268. [Google Scholar]
  3. Du H, Liu Y. Minmax-concave total variation denoising [J]. Signal, Image and Video Processing, 2018, 12(6): 1027-1034. [Google Scholar]
  4. Wang Y, Yang J, Yin W, 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]
  5. Jiao Y, Jin Q, Lu X, et al. Alternating direction method of multipliers for linear inverse problems [J]. SIAM Journal on Numerical Analysis, 2016, 54(4): 2114-2137. [Google Scholar]
  6. Chang H, Lou Y, Duan Y, et al. Total variation-based phase retrieval for Poisson noise removal [J]. SIAM Journal on Imaging Sciences, 2018, 11(1): 24-55. [CrossRef] [MathSciNet] [Google Scholar]
  7. Chan R H, Riemenschneider S D, Shen L, 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]
  8. Chan T, Marquina A, Mulet P. High-order total variation-based image restoration [J]. SIAM Journal on Scientific Computing, 2000, 22(2): 503-516. [CrossRef] [MathSciNet] [Google Scholar]
  9. Lefkimmiatis S, Ward J P, Unser M. Hessian Schatten-norm regularization for linear inverse problems [J]. IEEE Transactions on Image Processing, 2013, 22(5): 1873-1888. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  10. You Y L, Kaveh M. Fourth-order partial differential equations for noise removal [J]. IEEE Transactions on Image Processing, 2000, 9(10): 1723-1730. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  11. Lysaker M, Lundervold A, Tai X C. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time [J]. IEEE Transactions on Image Processing, 2003, 12(12): 1579-1590. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  12. Hu Y, Jacob M. Higher degree total variation (HDTV) regularization for image recovery [J]. IEEE Transactions on Image Processing, 2012, 21(5): 2559-2571. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  13. Shi Q, Sun N, Sun T, et al. Structure-adaptive CBCT reconstruction using weighted total variation and Hessian penalties [J]. Biomedical Optics Express, 2016, 7(9): 3299-3322. [Google Scholar]
  14. Papafitsoros K, Schönlieb C B. A combined first and second order variational approach for image reconstruction [J]. Journal of Mathematical Imaging and Vision, 2014, 48(2): 308-338. [Google Scholar]
  15. Bredies K, Kunisch K, Pock T. Total generalized variation [J]. SIAM Journal on Imaging Sciences, 2010, 3(3): 492-526. [CrossRef] [MathSciNet] [Google Scholar]
  16. Zhang J, Ma M, Wu Z, et al. High-order total bounded variation model and its fast algorithm for Poissonian image restoration [J]. Mathematical Problems in Engineering, 2019, 2019: 1-11. [Google Scholar]
  17. Liu X, Huang L. Total bounded variation-based Poissonian images recovery by split Bregman iteration [J]. Mathematical Methods in the Applied Sciences, 2012, 35(5): 520-529. [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  18. Adam T, Paramesran R, Mingming Y, et al. Combined higher order non-convex total variation with overlapping group sparsity for impulse noise removal [J]. Multimedia Tools and Applications, 2021, 80(12): 18503-18530. [Google Scholar]
  19. Jiang L, Huang J, Lv X G, et al. Alternating direction method for the high-order total variation-based Poisson noise removal problem [J]. Numerical Algorithms, 2015, 69(3): 495-516. [Google Scholar]
  20. Liu J, Huang T Z, Lv X G, et al. High-order total variation-based Poissonian image deconvolution with spatially adapted regularization parameter [J]. Applied Mathematical Modelling, 2017, 45: 516-529. [CrossRef] [MathSciNet] [Google Scholar]
  21. Wang X, Feng X, Wang W, et al. Iterative reweighted total generalized variation based Poisson noise removal model[J]. Applied Mathematics and Computation, 2013, 223: 264-277. [Google Scholar]
  22. Liu H, Tan S. Image regularizations based on the sparsity of corner points [J]. IEEE Transactions on Image Processing, 2018, 28(1): 72-87. [Google Scholar]
  23. Ono S, Miyata T, Yamada I. Cartoon-texture image decomposition using blockwise low-rank texture characterization [J]. IEEE Transactions on Image Processing, 2014, 23(3): 1128-1142. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  24. Liu P, Xiao L. Efficient multiplicative noise removal method using isotropic second order total variation [J]. Computers & Mathematics with Applications, 2015, 70(8): 2029-2048. [Google Scholar]
  25. Wang S, Huang T Z, Zhao X L, et al. Speckle noise removal in ultrasound images by first-and second-order total variation [J]. Numerical Algorithms, 2018, 78(2): 513-533. [Google Scholar]
  26. Mei J J, Huang T Z. Primal-dual splitting method for high-order model with application to image restoration [J]. Applied Mathematical Modelling, 2016, 40(3):2322-2332. [Google Scholar]
  27. Li F, Shen C, Fan J, et al. Image restoration combining a total variational filter and a fourth-order filter [J]. Journal of Visual Communication and Image Representation, 2007, 18 (4): 322-330. [CrossRef] [Google Scholar]
  28. Gabay D. Chapter IX applications of the method of multipliers to variational inequalities [J]. Studies in Mathematics & Its Applications, 1983, 15: 299-331. [CrossRef] [Google Scholar]
  29. Zhou W, Bovik A C, Sheikh H R, et al. Image quality assessment: From error visibility to structural similarity [J]. IEEE Trans Image Process, 2004, 13(4): 600-612. [NASA ADS] [CrossRef] [PubMed] [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.