An image denoising algorithm for mixed noise combining nonlocal means filter and sparse representation technique |
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| Affiliation: | 1. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, People’s Republic of China;2. School of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, People’s Republic of China;3. Tian Fu College of Southwestern University of Finance and Economic, Mianyang 621010, Sichuan, People’s Republic of China;1. Department of Applied Mathematics, University of Tarbiat Modares, P.O. Box 14115-175, Tehran, Iran;2. Department of Computer Science, University of Tarbiat Modares, P.O. Box 14115-175, Tehran, Iran;1. The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, No. 38 Zheda Road, Hangzhou, Zhejiang 310027, PR China;2. The Institute of Spacecraft System Engineering, No. 104 Youyi Road, Haidian, Beijing 100094, PR China;1. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China;2. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan;1. School of Information Engineering, Guangdong University of Technology, PR China;2. Fujian Provincial Key Laboratory of Data Mining and Applications, Fujian University of Technology, Fujian, PR China;1. Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA;2. Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China |
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| Abstract: | Nonlocal means (NLM) filtering or sparse representation based denoising method has obtained a remarkable denoising performance. In order to integrate the advantages of two methods into a unified framework, we propose an image denoising algorithm through skillfully combining NLM and sparse representation technique to remove Gaussian noise mixed with random-valued impulse noise. In the non-Gaussian circumstance, we propose a customized blockwise NLM (CBNLM) filter to generate an initial denoised image. Based on it, we classify the different noisy pixels according to the three-sigma rule. Besides, an overcomplete dictionary is trained on the initial denoised image. Then, a complementary sparse coding technique is used to find the sparse vector for each input noisy patch over the overcomplete dictionary. Through solving a more reasonable variational denoising model, we can reconstruct the clean image. Experimental results verify that our proposed algorithm can obtain the best denoising performance, compared with some typical methods. |
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| Keywords: | Image denoising Mixed noise Nonlocal means Noise classification Complementary sparse coding Sparse representation |
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