计算机与数字工程Issue(11)：2020-2023,2034,5.DOI:10.3969/j.issn.1672-9722.2015.11.028
基于稀疏表示的图像去噪算法优化
Image Denoising Algorithm Optimization Based on Sparse Representation
摘要
Abstract
Image denoising is very important in image restoration ,it is aimed to get a high quality image on the visual . Image denoising is the most basic problem in image processing .With the rise and popularization of compressed sensing ,more and more scholars begin to pay attention to the sparse representation theory and its applications ,image denoising based on image sparse representation has become a frontier research topics in this field in recent years .In this paper ,the model of nonlocally focused sparse representation is adopted for image restoration ,and the improved K-means algorithm and principle of PCA are combined creatively to form the atomic learning dictionary .By comparison with the classical algorithm ,it is found K-means that the algorithm can get higher PSNR and improve the smoothness of homogeneous regions while preserving edge and texture ,more features and other details ,and thus obtain better quality of image restoration .关键词
图像复原/图像去噪/稀疏表示/字典训练/峰值信噪比Key words
image restoration/image denoising/sparse representation/dictionary training/peak signal to noise ratio分类
信息技术与安全科学引用本文复制引用
黄欢,吴中骅..基于稀疏表示的图像去噪算法优化[J].计算机与数字工程,2015,(11):2020-2023,2034,5.基金项目
国家自然科学基金（编号61271007）资助。 （）
