Image Denoising via Sparse Representation Over Grouped Dictionaries With Adaptive Atom Size

Abstract

The classic K-SVD based sparse representation denoising algorithm trains the dictionary only with one fixed atom size for the whole image, which is limited in accurately describing the image. To overcome this shortcoming, this paper presents an effective image denoising algorithm with the improved dictionaries. First, according to both geometrical and photometrical similarities, image patches are clustered into different groups. Second, these groups are classified into the flat category, the texture category, and the edge category. In different categories, the atom sizes of dictionaries are designed differently. Then, the dictionary of each group is trained with the atom size determined by the category that the group belongs to and the noisy level. Finally, the denoising method is presented by using sparse representation over the constructed grouped dictionaries with adaptive atom size. Experimental results show that the proposed method achieves better denoising performance than related denoising algorithms, especially in image structure preservation.