CAI Bin1,2, LIU Wei2, ZHENG Zhong2, WANG Zengfu1,2
1.Department of Automation, University of Science and Technology of China, Hefei 230027 2.Laboratory of Nuclear Environment Telerobot, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031
Abstract Aiming at the problem of similarity calculation for image block in non-local means (NLM) denoising algorithm, a more accurate block matching algorithm is proposed. In this algorithm, the contribution of rotation is taken into alcount. To obtain the blocks similar to the neighborhood of the given pixel, the related blocks surrounding the given pixel are reordered according to their gray values, and then the pixels in the neighborhood of the given pixel are also reordered in the same way. Finally, the distance between the related blocks and the neighborhood of the given pixel are calculated according to the reordered gray values. The candidate blocks with small distance are selected. Furthermore, the more structurally similar blocks are selected from the candidate blocks. To eliminate the effect of noise, the inputted image is processed by a pre-filtering operation before similarity calculation. Simulation experiments show that compared with the original NLM denoising algorithm, the proposed algorithm has better performances in peak signal-to-noise ratio (PSNR), mean structural similarity (MSSIM) and subjective visual effect. Especially, the proposed algorithm has better denoising performance for the images with lots of noise variance.
About author:: (CAI Bin， born in 1990， master student. His research interests include image denoising and 3D reconstruction.)(LIU Wei， born in 1987， master， assistant professor. His research interests include image denoising and image fusion.)(ZHENG Zhong， born in 1981， Ph.D.， associate professor. His research interests include pattern recognition and intelligent robot.)(WANG Zengfu (Corresponding author)， born in 1960， Ph.D.， professor. His research interests include computer vision， computer audio， pattern recognition and intelligent robot.)
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2016, Vol. 29 
