Image denoising based on structure clustering
基于结构聚类的图像去噪

In order to overcome the deficiencies of traditional denoising algorithm BM3D, this paper proposed denoising algorithm based on structure clustering according to the local structural similarity. First, processing coarse clustering to get block group according to the mean, followed by the use of robust data normalization to construct structure similar subgroup. At last, denoising the subgroup, if subgroup capacity is greater than one, using BM3D to denoise the subgroup, on the contrary, using DCT denoising algorithm based on the threshold to denoise the block. The experimental results show that the algorithm protects the structure of the image information and improves the image visual effects compared with traditional BM3D algorithm.