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.
蔡斌，刘卫，郑重，汪增福. 一种改进的非局部均值去噪算法*[J]. 模式识别与人工智能, 2016, 29(1): 1-10.
CAI Bin, LIU Wei, ZHENG Zhong, WANG Zengfu. An Improved Non-local Means Denoising Algorithm. , 2016, 29(1): 1-10.
[1] 阮秋琦.数字图像处理学.北京:电子工业出版社, 2004. (RUAN Q Q. Digital Image Processing. Beijing, China: Publishing House of Electronics Industry, 2004.) [2] HUANG H C, LEE T C M. Data Adaptive Median Filters for Signal and Image Denoising Using a Generalized SURE Criterion. IEEE Signal Processing Letters, 2006, 13(9): 561-564. [3] YUAN S Q, TAN Y H. Impulse Noise Removal by a Global-Local Noise Detector and Adaptive Median Filter. Signal Processing, 2006, 86(8): 2123-2128. [4] SENDUR L, SELESNICK I W. Bivariate Shrinkage Functions for Wavelet-Based Denoising Exploiting Interscale Dependency. IEEE Trans on Signal Processing, 2002, 50(11): 2744-2756. [5] PORTILLA J, STRELA V, WAINWRIGHT M J, et al. Image Denoising Using Scale Mixtures of Gaussians in the Wavelet Domain. IEEE Trans on Image Processing, 2003, 12(11): 1338-1351. [6] 纪 建,徐双星,李 晓.基于形态成分分析和Contourlet变换的自适应阈值图像去噪方法.模式识别与人工智能, 2014, 27(6): 561-568. (JI J, XU S X, LI X. An Adaptive Thresholding Image Denoising Method Based on Morphological Component Analysis and Contourlet Transform. Pattern Recognition and Artificial Intelligence, 2014, 27(6): 561-568.) [7] 凤宏晓,侯 彪,焦李成,等.基于非下采样Contourlet域局部高斯模型和MAP的SAR图像相干斑抑制.电子学报, 2010, 38(4): 811-816. (FENG H X, HOU B, JIAO L L. SAR Image Despeckling Based on Local Gaussian Model and Map in NSCT Domain. Acta Electronica Sinica, 2010, 38(4): 811-816.) [8] YIN M, LIU W, ZHAO X, et al. Image Denoising Using Trivariate Prior Model in Nonsubsampled Dual-Tree Complex Contourlet Transform Domain and Non-local Means Filter in Spatial Domain. Optik-International Journal for Light and Electron Optics, 2013, 124(24): 6896-6904. [9] ELAD M, AHARON M. Image Denoising via Sparse and Redundant Representations over Learned Dictionaries. IEEE Trans on Image Processing, 2006, 15(12): 3736-3745. [10] SUN D, GAO Q W, LU Y X, et al. A Novel Image Denoising Algorithm Using Linear Bayesian MAP Estimation Based on Sparse Representation. Signal Processing, 2014, 100: 132-145. [11] BUADES A, COLL B, MOREL J M. A Non-local Algorithm for Image Denoising // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005, II: 60-65. [12] TASDIZEN T. Principal Neighborhood Dictionaries for Nonlocal Means Image Denoising. IEEE Trans on Image Processing, 2009, 18(12): 2649-2660. [13] GREWENIG S, ZIMMER S, WEICKERT J. Rotationally Invariant Similarity Measures for Nonlocal Image Denoising. Journal of Vi-sual Communication and Image Representation, 2011, 22(2): 117-130. [14] 孙伟峰,彭玉华.一种改进的非局部平均去噪方法.电子学报, 2010, 38(4): 923-928. (SUN W F, PENG Y H. An Improved Non-local Means De-noising Approach. Acta Electronica Sinica, 2010, 38(4): 923-928.) [15] DELEDALLE C A, DENIS L, TUPIN F. Iterative Weighted Maximum Likelihood Denoising with Probabilistic Patch-Based Weights. IEEE Trans on Image Processing, 2009, 18(12): 2661-2672. [16] DABOV K, FOI A, KATKOVNIK V, et al. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering. IEEE Trans on Image Processing, 2007, 16(8): 2080-2095. [17] YUE W, TRACEY B, NATARAJAN P, et al. Probabilistic Non-local Means. IEEE Signal Processing Letters, 2013, 20(8): 763-766. [18] DELEDALLE C A, DUVAL V, SALMON J. Non-local Methods with Shape-Adaptive Patches (NLM-SAP). Journal of Mathematical Imaging and Vision, 2012, 43(2): 103-120. [19] MAHMOUDI M, SAPIRO G. Fast Image and Video Denoising via Nonlocal Means of Similar Neighborhoods. IEEE Signal Processing Letters, 2006, 12(12): 839-842. [20] COUP P, YGER P, PRIMA S, et al. An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Resonance Images. IEEE Trans on Medical Imaging, 2008, 27(4): 425-441. [21] 许光宇,檀结庆,钟金琴.自适应的有效非局部图像滤波.中国图象图形学报, 2012, 17(4): 471-479. (XU G Y, TAN J Q, ZHONG J Q. Adaptive Efficient Non-local Image Filtering. Journal of Image and Graphics, 2012, 17(4): 471-479.) [22] LIU Y L, WANG J, CHEN X, et al. A Robust and Fast Non-local Means Algorithm for Image Denoising. Journal of Computer Science and Technology, 2007, 23(2): 270-279. [23] WANG Z, BOVIK A C, SHEIKH H R, et al. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans on Image Processing, 2004, 13(4): 600-612. [24] FOI A, KATKOVNIK V, EGIAZARIAN K. Pointwise Shape-Adaptive DCT for High Quality Denoising and Deblocking of Grayscale and Color Images. IEEE Trans on Image Processing, 2007, 16(5): 1395-1411.
2016, Vol. 29 
