一种基于去冗余字典的图像去噪算法  

An Image De-noising Algorithm Based on Redundance Removed Dictionary

作　　者：张丹莹[1] 李翠华[1] 李雄宗[1] 施华[1] 张东晓[1] 

机构地区：[1]厦门大学信息科学与技术学院,福建厦门361005

出　　处：《厦门大学学报（自然科学版）》2012年第4期691-695,共5页Journal of Xiamen University：Natural Science

基　　金：国防基础科研计划项目;国防科技重点实验室基金项目;福建省自然科学基金项目(2011J01365);高等学校博士学科点专项科研基金项目(20110121110020)

摘　　要：图像去噪是图像处理中的关键问题之一,也是图像后续处理的基础.结合近年来兴起的稀疏表示理论,能更好的处理图像去噪问题.在正交匹配追踪(orthogonal matching pursuit,OMP)的基础上,采用K-奇异值分解(K-SVD)算法对图像进行去噪.为了得到更好的去噪效果,改进了字典更新算法,对字典原子进行优化选择,去除冗余的字典原子,并用图像块替换字典原子,用于提高字典训练的效率,与自然图像数据相适应.实验结果表明,与小波去噪算法相比,该算法具有良好的去噪能力,能较好地保持图像的细节和边缘特征,去噪后的图像更为清晰.Image denoising is one of the key issues in the image processing and the foundation of further research. Combined with the sparse representation theory, which emerged in recent year,we can handle the image denoising problems better. Based on orthogonal matching pursuit（OMP） algorithm, this paper used K singular value decomposition（K-SVD） algorithm for image de-noising. In or der to get better de-noising performance, this paper improves dictionary updating algorithm. The core provides a more optimal choice for training of the dictionary atoms, replaces the useless and redundant dictionary of atoms with natural image patch dictionary at-oms. By this way, we improve the training of the dictionary effectively, and adapt to natural image. Experimental results show that, compare with the wavelet de-noising algorithm, this algorithm has a good de-noising ability, while keeping the detail and the edge character of the image better, make the de-noising image clear.

关 键 词：超完备字典 稀疏表示 去噪 正交匹配追踪 奇异值分解 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]