Image denoising via an improved non‐local total variation model-Reference-Cited by-同舟云学术

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Image denoising via an improved non‐local total variation model

Published:2018-08 Issue:8 Volume:2018 Page:745-752
ISSN:2051-3305
Container-title:The Journal of Engineering
language:en
Short-container-title:J. eng.

Author:

Bai Yunjiao¹,Liu Yi¹,Zhang Quan¹,Jia Lina¹²,Gui Zhiguo¹

Affiliation:

1. Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data, North University of ChinaTaiyuan 030051People's Republic of China

2. State Key Laboratory of Explosion Science and Technology, Beijing Institute of TechnologyBeijing 100081People's Republic of China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Institution of Engineering and Technology (IET)

Subject

General Engineering,Energy Engineering and Power Technology,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/joe.2017.0388

Reference26 articles.

1. An adaptive diffusion coefficient selection for image denoising;Rafsanjani H.K.;Digit. Signal Process.,2017

2. Weighted and well‐balanced anisotropic diffusion scheme for image denoising and restoration;Surya Prasath V.B.;Nonlinear Anal. Real World Appl.,2014

3. Improved anisotropic diffusion equation based on new non‐local information scheme for image denoising;Yuan J.;IET Comput. Vis.,2015

4. Nonlinear total variation based noise removal algorithms;Rudin L.I.;Physica D,1992

5. Image denoising using total variation model guided by steerable filter;Zhang W.;Math. Problems Eng.,2014

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