摘要
K-奇异值分解(K-SVD)算法在强噪声下的去噪性能较差。为此,提出一种新的图像去噪算法。使用相关系数匹配准则和噪声原子裁剪方法改进传统K-SVD算法,提高原算法的去噪性能,将非局部正则项融入图像去噪模型,并采用非局部自相似性进一步改善图像的去噪效果。实验结果表明,与传统K-SVD算法相比,该算法在提高同质区域平滑性的同时,能保留更多的纹理、边缘等细节特征。
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.
出处
《计算机工程》
CAS
CSCD
北大核心
2015年第5期249-253,共5页
Computer Engineering
基金
国家自然科学基金资助项目(61372145)
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
image denoising
sparse representation
Singular Value Decomposition （ SVD ）
Orthonomal MatchingPursuit （OMP） algorithm
dictionary optimization
non-local self-similarity
