自动化学报Issue(2)：439-444,6.DOI:10.16383/j.aas.2015.c130909
基于分块奇异值分解的两级图像去噪算法
Two-stage Image Denoising Using Patch-based Singular Value Decomposition
摘要
Abstract
This paper presents an efficient patch-based image denoising scheme by using singular value decomposition (SVD). In this scheme, similar image patches from a noisy image are simply grouped together. For a better sparse representation of these similar patches, firstly, the 2-D SVD is utilized to reveal the essential features of each individual patch, and then the 1-D SVD is utilized to exploit the correlation between similar patches. By doing so, the image features can be well preserved when attenuating the noise by the shrinkage of transform co-efficients. To further improve the denoising performance, the proposed scheme is employed once again. But the similar patch grouping is performed from the first-stage estimated image and a fixed orthogonal transform instead of 1-D SVD is adopted to remove the redundancy shared by similar patches. Experimen-tal results show that the proposed two-stage denoising scheme achieves more competitive performance than the state-of-the-art denoising algorithms, especially in preserving image details and introducing very few artifacts.关键词
奇异值分解/图像去噪/相似块分组/图像纹理细节Key words
Singular value decomposition (SVD)/image de-noising/similar patch grouping/image details引用本文复制引用
刘涵,梁莉莉,黄令帅..基于分块奇异值分解的两级图像去噪算法[J].自动化学报,2015,(2):439-444,6.基金项目
国家自然科学基金(61174101,61403305),高等学校博士学科点专项科研基金(2012611811004,2013611812005),陕西省教育厅科研计划项目(14JK1543)资助@@@@Supported by National Natural Science Foundation of China (61174101,61403305), Specialized Research Fund for the Doctoral Program of Higher Education (2012611811004,2013611812005), and Scientific Research Program Funded by Shaanxi Provincial Educa-tion Department (14JK1543) （61174101,61403305）
