关键词: K均值聚类, 稀疏分解, 图像去噪, 正交匹配追踪, 过完备字典

Abstract: The sparse representations of signal theory has been extensively and deeply researched in recent years, and been widely applied to image processing. For the huge computation of over-complete dictionary structure and sparse decomposition, a new self-adaptive method for image denoising based on sparse decomposition and clustering was proposed. Firstly, an overcomplete dictionary was designed by training samples with a modified K-means clustering algorithm. In the training process, atoms of the dictionary were updated adaptively in every iterative step to better fit the sparse representation of the samples. Secondly, the sparse representation of the test image was obtained by using the dictionary combined with Orthogonal Matching Pursuit (OMP) algorithm, so as to achieve image denoising. The experimental results show that in terms of image denoising and computational complexity, the performance of the proposed method is better than the traditional dictionary training algorithm.

Key words: K-means clustering, sparse decomposition, image denoising, Orthogonal Matching Pursuit (OMP), overcomplete dictionary