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Volume 45 Issue 3
Mar.  2019
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Journal of Beijing University of Aeronautics and Astronautics  > 2019  >  45(3): 464-471.
PANG Zhifeng, ZHANG Huili, SHI Baoliet al. Image denoising model based on directional total variation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(3): 464-471. doi: 10.13700/j.bh.1001-5965.2018.0329(in Chinese)
Citation: PANG Zhifeng, ZHANG Huili, SHI Baoliet al. Image denoising model based on directional total variation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(3): 464-471. doi: 10.13700/j.bh.1001-5965.2018.0329(in Chinese)
shu

Image denoising model based on directional total variation

doi: 10.13700/j.bh.1001-5965.2018.0329

  • School of Mathematics and Statistics, Henan University, Kaifeng 475004, China

Funds:

National Basic Research Program of China 2015CB856003

National Natural Science Foundation of China 11401170

National Natural Science Foundation of China U1304610

Engineering Mathematical Modeling and Analysis, Foundation for Key Laboratory of Hunan Province Changsha University of Science and Technology

Henan University Excellent Youth Cultivation Project yqpy20170062

More Information
  • Corresponding author: PANG Zhifeng, E-mail:zhifengpang@163.com
  • Received Date: 04 Jun 2018
  • Accepted Date: 03 Sep 2018
  • Publish Date: 20 Mar 2019
    Abstract

  • For the problem of texture image denoising, by analyzing the advantages and disadvantages of the total variation (TV) denoising model and the directional total variation (DTV) denoising model, we propose a robust denoising model based on directional total variation. In the proposed model, in order to efficiently characterize the different structural features in the image, the exponential p in the edge adaptive directional total variation regularization term can be availably chosen in (0, 2) based on the structure in the image. Since the proposed model is a non-smooth convex optimization with separable operator, it can be solved by using the alternating direction method of multipliers (ADMM). Then the convergence of the numerical method can be efficiently kept. Compared with other classic models, numerical implementations show that the proposed model can achieve higher peak signal-to-noise ratio and structural similarity, and can effectively retain image details while removing noise.

     

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