Progressive Image Denoising Algorithm

Authors

Haiyang Li, 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; ;2. University of Chinese Academy of Sciences, Beijing 100049, China;
Weiguo Cao, 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; ;2. University of Chinese Academy of Sciences, Beijing 100049, China;
Shirui Li, 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; ;2. University of Chinese Academy of Sciences, Beijing 100049, China;
Kelu Tao, 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; ;2. University of Chinese Academy of Sciences, Beijing 100049, China;
Li Hua, 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; ;2. University of Chinese Academy of Sciences, Beijing 100049, China;

Abstract

Abstract: Currently almost all denoising algorithms are implemented by processing original noisy image itself simply, which could not enhance the performance further by combining original noisy image with the denoised image. To solve the problem, a framework of progressive image denoising method was proposed. The framework is based on the block matching and 3D collaborative filtering (BM3D) algorithm, which has the most remarkable denoising effect. It includes three layers and two fusions. Each layer is implemented by BM3D and denoises the fused image generated from the previous layers. Adequate statistical results show that under the same noise condition, our proposed method and another new algorithm can improve original BM3D on PSNR to different degrees, but ours has a better performance. As the noise increases, the performance improvement is more remarkable, which means that the proposed method can improve CT imaging quality and obtain good results.

Recommended Citation

Li, Haiyang; Cao, Weiguo; Li, Shirui; Tao, Kelu; and Hua, Li (2020) "Progressive Image Denoising Algorithm," Journal of System Simulation: Vol. 29: Iss. 2, Article 7.
DOI: 10.16182/j.issn1004731x.joss.201702007
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss2/7

First Page

282

Revised Date

2015-09-01

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201702007

Last Page

294

CLC

TP391.9

Recommended Citation

Li Haiyang, Cao Weiguo, Li Shirui, Tao Kelu, Li Hua. Progressive Image Denoising Algorithm[J]. Journal of System Simulation, 2017, 29(2): 282-294.

DOI

10.16182/j.issn1004731x.joss.201702007