Denosing Model Based on Double-Fidelity Total Variation

[2] Zhou C H, Wang Z L, Liu S K. Method of image restoration directly based on spatial varied point spread function[J]. Acta Optica Sinica, 37, 0110001(2017).

[3] Tan Z G, Ou J P, Zhang J et al. A laminar denoising algorithm for depth image[J]. Acta Optica Sinica, 37, 0510002(2017).

[4] Yu L Q, Qin Y L, Zhang X S. Denoising of strong noisy image via gradient reweighted non-local averaging over learned dictionaries[J]. Laser & Optoelectronics Progress, 53, 111002(2016).

[5] Wang X D. Study on image denoising models based on MAP estimation, variation and PDE Xi'an:[D]. Xidian University(2013).

[6] Wang W J, Yu X L, Ma W S et al. Aspheric interferogram image denoising method based on the improved P-M modeland weighted composite median filter[J]. Laser & Optoelectronics Progress, 53, 031002(2016).

[7] Li Q Q. Study of image denoising model based on regularization methoed[D]. Wuhan: Wuhan University of Technology(2013).

[8] Jain P, Tyagi V. A survey of edge-preserving image denoising methods[J]. Information Systems Frontiers, 18, 159-170(2016). http://dl.acm.org/citation.cfm?id=2898148

[9] Yan X F, Xu T F, Bai T Z. Varying-parameter Tikhonov regularization image restoration[J]. Laser & Optoelectronics Progress, 50, 051001(2013).

[10] Jia Z G, Wei M S. A new TV-Stokes model for image deblurring and denoising with fast algorithms[J]. Journal of Scientific Computing, 72, 522-541(2017). http://dl.acm.org/citation.cfm?id=3128614.3128635

[11] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[C]. Eleventh International Conference of the Center for Nonlinear Studies on Experimental, 259-268(1992).

[12] Niu S Y, Chen P, Pan J X et al. A soft-threshold filtering EM-TV algorithm for CT reconstruction[J]. Optik-International Journal for Light and Electron Optics, 126, 202-207(2015). http://www.sciencedirect.com/science/article/pii/S0030402614012078

[13] Micchelli C A, Shen L X, Xu Y S et al. Proximity algorithms for the L1/TV image denoising model[J]. Advances in Computational Mathematics, 38, 401-426(2013). http://link.springer.com/article/10.1007/s10444-011-9243-y

[14] Tang L M, Fang Z. Edge and contrast preserving in total variation image denoising[J]. Eurasip Journal on Advances in Signal Processing, 16, 13-24(2016). http://link.springer.com/article/10.1186/s13634-016-0315-5

[15] Bergmann R, Weinmann A. A second-order TV-type approach for inpainting and denoising higher dimensional combined cyclic and vector space data[J]. Journal of Mathematical Imaging and Vision, 55, 401-427(2016). http://dl.acm.org/citation.cfm?id=2931954

[16] Zhang H Y, Peng Q Z. Adaptive image denoising model based on total variation[J]. Opto-Electronic Engineering, 33, 50-53(2006).

[17] Wang L. Image denoising model based on variable norm of total variation[D]. Kunming: Kunming University of Science and Technology(2016).

[18] Zhu L X, Wang P A, Xia D S. Nonlinear diffusion based image denoising coupling gradient fidelity term[J]. Journal of Computer Research and Development, 44, 1390-1398(2007).

[19] Ai L. Improved anisotropic total variation denoising model[J]. Computer Engineering and Applications, 52, 192-195(2016).

[20] Ren F Q, Qiu T S. Blurred image restoration method based on second-order total generalized variation regularization[J]. Acta Automatica Sinica, 41, 1166-1172(2015).

[21] Wang L L, Tang C, Wang Y J. Impact on partial differential equations filtering models of difference scheme[J]. Laser & Optoelectronics Progress, 52, 091004(2015).

[22] Man J J, Shen J. A method of image noise estimate[J]. Journal of Natural Science of Hunan Normal University, 31, 44-47(2008).