关键词: 非局部去噪, 稀疏表示, 联合滤波, 字典学习

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 reinforces 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.

Key words: non-local denoising, sparse representation, collaborative filter, dictionary learning