计算机工程Issue(5)：249-253,5.DOI:10.3969/j.issn.1000-3428.2015.05.046
基于改进K-SVD和非局部正则化的图像去噪
Image Denoising Based on Improved K-SVD and Non-local Regularization
摘要
Abstract
In view of the poor performance of the K-Singular Value Decomposition( K-SVD) denoising method,a new algorithm is proposed. The denoising performance is improved by the refined K-SVD method with the help of the correlation coefficient matching criterion and dictionary cutting method. By combining the non-local self-similarity as a constrained regularization into the image denoising model,the performance is further enhanced. Experimental results show that compared with traditional K-SVD method, this algorithm can effectively improve the smoothness of homogeneous regions with preserving more texture and edge details.关键词
图像去噪/稀疏表示/奇异值分解/正交匹配追踪算法/字典优化/非局部自相似性Key words
image denoising/sparse representation/Singular Value Decomposition (SVD)/Orthonomal Matching Pursuit(OMP) algorithm/dictionary optimization/non-local self-similarity分类
信息技术与安全科学引用本文复制引用
杨爱萍,田玉针,何宇清,董翠翠..基于改进K-SVD和非局部正则化的图像去噪[J].计算机工程,2015,(5):249-253,5.基金项目
国家自然科学基金资助项目(61372145)。 （61372145）
