【摘要】 基于超完备字典的图像稀疏表示是一种新的图像表示理论,利用超完备字典的冗余性可以有效地捕捉图像的各种结构特征,从而实现图像的有效表示。当前稀疏表示的理论研究主要集中在稀疏分解算法和字典构造算法两方面。本文提出一种新的超完备字典构造算法:K-LMS算法,该算法由K均值聚类算法泛化获得,可用于超完备字典的自适应更新,以实现图像的有效表示。针对图像去噪问题,本文给出一种基于超完备稀疏表示的去噪方法,该方法利用图像在超完备字典上的自适应稀疏分解,通过阈值处理的方法实现了图像去噪,实验结果证实了本文所提方法的有效性。更多还原

【Abstract】 The sparse representation based on over-complete dictionary is a new image representation theory.The redundancy of over-complete dictionary can make it effectively capture the geometrical characteristics of the images.Recent activities in this field concentrate mainly on the study of sparse decomposition algorithm and dictionary design algorithm.In this paper we propose a novel dictionary design algorithm,the K-LMS algorithm.It was obtained from generalizing the K-Means clustering algorithm and can be used in adaptive updating of over-complete dictionary in order to achieve sparse signal representations.Aiming at image denoising,a method based on over-complete sparse representation theory is introduced.With the application of image sparse representation in over-complete dictionary,the proposed algorithm reconstructs a simple threshold to realize image denoising.Experimental results demonstrate the effectivity of the proposed method.更多还原