Image Denoising Algorithm Based on Method Noise Sparse Representation Dictionary Learning

Authors

Lishao Huang, College of Electronics and Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China;
Haiying Wen, College of Electronics and Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China;
Sisi Gu, College of Electronics and Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, China;

Abstract

Abstract: For the shortcoming of losting partial texture information with image denoising process, the image denoising algorithm based on method noise sparse representation was proposed. The method noise, which was defined as the difference between the noisy and the denoised image, was obtained by guided filter. Then redundant dictionary was learned by improved dictionary learning method and the method noise. The image texture information in method noise was extracted by the learning dictionary, and image was restored by the extracted image texture information and denoised image by guided filter. The experimental results demonstrate that the peak signal to noise ratio Value (PSNR) of the proposed algorithm is better than state-of-the-art algorithms, while the proposed algorithm can well preserve the texture information in the denoised image, making them look more natural.

Recommended Citation

Huang, Lishao; Wen, Haiying; and Gu, Sisi (2020) "Image Denoising Algorithm Based on Method Noise Sparse Representation Dictionary Learning," Journal of System Simulation: Vol. 28: Iss. 1, Article 21.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss1/21

First Page

154

Revised Date

2015-07-22

DOI Link

https://doi.org/

Last Page

159

CLC

P391.41

Recommended Citation

Huang Lishao, Wen Haiying, Gu Sisi. Image Denoising Algorithm Based on Method Noise Sparse Representation Dictionary Learning[J]. Journal of System Simulation, 2016, 28(1): 154-159.