1.Abergel, R. and Moisan, L. The Shannon total variation, J. Math. Imaging Vis., 59(2) (2017), 341–370.
2. Asim, M., Shamshad, F. and Ahmed, A. Blind image deconvolution using deep generative priors, IEEE Trans. Comput. Imaging, 6 (2020), 1493–1506.
3. Bhargava, G.U. and Gangadharan, S.V. FPGA implementation of modified recursive box filter-based fast bilateral filter for image denoising, Circuits, Syst. Signal Process., 40(3) (2021), 1438–1457.
4. Bioucas-Dias, J.M. and Figueiredo, M.A. A new TwIST: Two-step itera- tive shrinkage/thresholding algorithms for image restoration, IEEE Trans. Image Process., 16(12) (2007), 2992–3004.
5. Bredies, K., Kunisch, K. and Pock, T. Total generalized variation, SIAM J. Imaging Sci. , 3(3) (2010), 492–526.
6. Bredies, K. and Valkonen, T. Inverse problems with second-order total generalized variation constraints, Proceedings of SampTA, 201 (2011).
7. Burns, M., Haidacher, M., Wein, W., Viola, I. and Groller, M. E. Feature emphasis and contextual cutaways for multimodal medical visualization, EuroVis, 7 (2007), 275–282.
8. Caselles, V., Chambolle, A. and Novaga, M. The discontinuity set of solu-tions of the TV denoising problem and some extensions, Multiscale Model Simul. 6(3) (2007), 879–894.
9. Caselles, V., Chambolle, A. and Novaga M., Regularity for solutions of the total variation denoising problem, Rev. Mat. Iberoam., 27(1) (2011), 233–252.
10. Chambolle, A., Levine, S.E. and Lucier, B.J. An upwind finite-difference method for total variation-based image smoothing, SIAM J. Imaging Sci. 4(1) (2011), 277–299.
11. Chambolle, A. and Pock, T. A first-order primal-dual algorithm for con-vex problems with applications to imaging, J. Math. Imaging Vis., 40(1)(2011), 120–145.
12. Chan, T.F., Esedoglu, S. and Park, F.E. Image decomposition combin-ing staircase reduction and texture extraction, J. Vis. Commun. Image Represent., 18(6) (2007), 464–486.
13. Chan, T.F. and Wong, C.K. Total variation blind deconvolution, IEEE Trans. Image Process., 7(3) (1998), 370–375.
14. Chan, T., Marquina, A. and Mulet, P. High-order total variation-based image restoration, SIAM J. Sci. Comput. 22(2) (2000), 503–516.
15. Chen, Y., Liu, L., Tao, J., Xia, R., Zhang, Q., Yang, K., Xiong, J. and Chen, X. The improved image inpainting algorithm via encoder and similarity constraint, Vis. Comput. 37(7) (2021), 1691–1705.
16. Condat, L. Discrete total variation: New definition and minimization, SIAM J. Imaging Sci. , 10(3) (2017), 1258–1290.
17. Dong, J. and Pan, J. Deep outlier handling for image deblurring, IEEE Trans. Image Process., 30 (2021), 1799–1811.
18. El Hamidi, A., Menard, M., Lugiez, M. and Ghannam, C. Weighted and extended total variation for image restoration and decomposition, Pattern Recognit. 43(4) (2010), 1564–1576.
19. Fang, F., Li, J., Yuan, Y., Zeng, T., Zhang, G. Multi-level edge features guided network for image denoising, IEEE Trans. Neural Netw. Learn. Syst., 32(9) (2021), 3956–3970.
20. Guo, W., Qin, J. and Yin, W. A new detail-preserving regularization scheme, SIAM J. Imaging Sci. , 7(2) (2014), 1309–1334.
21. Kang, S.H. and March, R. Variational models for image colorization via chromaticity and brightness decomposition, IEEE Trans. Image Process., 16(9) (2007), 2251–2261.
22. Liu, J., Yan, M. and Zeng, T. Surface-aware blind image deblurring, IEEE Trans. Pattern Anal. Mach. Intell., 43(3) (2021), 1041–1055.
23. Liu, X. Total generalized variation and wavelet frame-based adaptive im-age restoration algorithm, Vis. Comput. 35(12) (2019), 1883–1894.
24. Malgouyres, F. and Guichard, F. Edge direction preserving image zoom-ing: a mathematical and numerical analysis, SIAM J. Numer. Anal., 39(1)(2001), 1–37.
25. Maso, G.D., Fonseca, I., Leoni, G. and Morini, M. A higher order model for image restoration: the one-dimensional case, SIAM J. Math. Anal., 40(6) (2009), 2351–2391.
26. Moisan, L. How to discretize the total variation of an image?, in the 6th International Congress on Industrial Applied Mathematics, Proceedings in Applied Mathematics and Mechanics, 7(1) (2007), 1041907–1041908.
27. Nacereddine, N., Zelmat, M., Belaifa, S.S. and Tridi, M. Weld defect detection in industrial radiography based digital image processing, Trans. Eng. Technol. Educ., 2 (2005), 145–148.
28. Rajora, S., Butola, M. and Khare, K. Regularization-parameter-free opti-mization approach for image deconvolution, Applied Optics. 60(19) (2021),5669–5677.
29. Reddy, P.L. and Pawar, S. Multispectral Image Denoising Using Curvelet Transform and Kriging Interpolation Based Wiener Filter, Design Engi-neering, (2021), 838–849.
30. Rudin, L.I., Osher S. and Fatemi, E. Nonlinear total variation based noise removal algorithms, ’Phys. D: Nonlinear Phenom., 60(1-4) (1992), 259–268.
31. Russ JC. The image processing handbook, 6th ed. USA: CRC Press, 2011.
32. Sakthidasan alias Sankaran, K. and Nagarajan, V. Noise removal through the exploration of subjective and apparent denoised patches using discrete wavelet transform, IETE J. Res., 67(6) (2021), 843–852.
33. Samson, C., Blanc-Féraud, L., Aubert, G. and Zerubia, J. A variational model for image classification and restoration, IEEE Trans. Pattern Anal. Mach. Intell., 22(5) (2000), 460–472.
34. Scherzer, O. Denoising with higher order derivatives of bounded variation and an application to parameter estimation, Computing, 60(1) (1998), 1–27.
35. Setzer, S.I. and Steidl, G.A. Variational methods with higher order deriva-tives in image processing, Approximation, 12 (2008), 360–386.
36. Valsesia, D. and Boufounos, P. T. Multispectral image compression using universal vector quantization, IEEE Information Theory Workshop (ITW), (2016), 151–155.
37. Vogel, C.R. and Oman, M.E. Fast, robust total variation-based recon-struction of noisy, blurred images, IEEE Trans. Image Process., 7(6)(1998), 813–824.
38. Wang, N., Zhang, Y. and Zhang, L. Dynamic selection network for image inpainting, IEEE Trans. Image Process., 30 (2021), 1784–1798.
39. Wang, W., Zhang, C. and Ng, M.K. Variational model for simultaneously image denoising and contrast enhancement, Opt. Express, 28(13) (2020),18751–18777.
40. Wang, Y., Song, X. and Chen, K. Channel and space attention neural network for image denoising, IEEE Signal Process. Lett., 28 (2021), 424–428.
41. Wang, Z., Bovik, A.C., Sheikh, H.R. and Simoncelli, E.P. Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process., 13(4) (2004), 600–612.
42. Wu, T., Gu, X., Wang, Y. and Zeng, T. Adaptive total variation based image segmentation with semi-proximal alternating minimization, Signal Process. 183 (2021), 108017.
43. Wu, T. and Shao, J. Non-convex and convex coupling image segmenta-tion via tgpv regularization and thresholding, Adv. Appl. Math. Mech., 12 (2020), 849–878.
44. Wu, T., Shao, J., Gu, X., Ng, M.K. and Zeng, T. Two-stage image segmentation based on nonconvex l2 − lp approximation and thresholding, Appl. Math. Comput., 403 (2021), 126168.
45. Yuan, J. An improved variational model for denoising magnetic resonance images,Comput. Math. Appl. , 76(9) (2018), 2212–2222.
46. Yuan, Q., Zhang, L. and Shen, H. Multiframe super-resolution employ-ing a spatially weighted total variation model, IEEE Trans. Circuits Syst. Video Technol., 22(3) (2011), 379–392.
47. Zhang, H., Chen, H., Yang, G. and Zhang, L. LR-Net: Low-rank spatial-spectral network for hyperspectral image denoising, IEEE Trans. Image Process., 30 (2021), 8743–8758.
48. Zhang, X. Two-step non-local means method for image denoising, Multi-dimens. Syst. Signal Process., (2021), 1–26.
49. Zhang, Y., Li, K., Li, K., Sun, G., Kong, Y. and Fu, Y. Accurate and fast image denoising via attention guided scaling, IEEE Trans. Image Process., 30 (2021), 6255–6265.
50. Zhang, Z. and Blum, R.S. Region-based image fusion scheme for con-cealed weapon detection, in Proceedings of the 31st Annual Conference on Information Sciences and Systems, (1997), 168–173.
