计算机工程与应用2015，Vol.51Issue(24)：180-185,6.DOI:10.3778/j.issn.1002-8331.1312-0166
基于联合滤波的聚类稀疏表示图像去噪算法
Image denoising via clustering-based sparse representation over collaborative filter
摘要
Abstract
For the influence of noise for clustering in non-local means denoising algorithm, a denoising algorithm based on collaborative filter and clustering-based sparse representation is presented. It employs Wiener filter and Butterworth filter to extract high-frequency components on the noisy image, and simultaneously reduces the influence of noise for clustering. The high-frenquency image blocks that are extracted from the noisy image are clustered by using the non-local means denoising. The adaptive ability of dictionary is enhanced because each block runs dictionary learning independently. Then structured dictionaries are learned by using several dictionary update cycles-based K-SVD instead of K-SVD. It rein-forces the descriptive ability of dictionary. The experiments show that the modified algorithm, which is compared with the traditional K-SVD denoising algorithm, can protect the information of image structure effectively and promote the result of denoising greatly.关键词
非局部去噪/稀疏表示/联合滤波/字典学习Key words
non-local denoising/sparse representation/collaborative filter/dictionary learning分类
信息技术与安全科学引用本文复制引用
高美凤,王晨..基于联合滤波的聚类稀疏表示图像去噪算法[J].计算机工程与应用,2015,51(24):180-185,6.基金项目
国家自然科学基金(No.61104092) （No.61104092）
江苏省产学研前瞻性联合研究项目(No.BY2012066). （No.BY2012066）
