Detail preserving image denoising with patch-based structure similarity via sparse representation and SVD

• Published in: Computer vision and image understanding Vol. 206; p. 103173
• Main Authors: Shi, Miaowen, Zhang, Fan, Wang, Suwei, Zhang, Caiming, Li, Xuemei
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.05.2021
• Subjects: Image denoising; Singular value decomposition( SVD); Sparse representation; Structure similarity; Sparse representation; Structure similarity; Singular value decomposition( SVD); Image denoising
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2021.103173

The key problem of image denoising methods is to smooth noise while retaining the details of original image. The human vision system is more sensitive to the details (or the high frequency components) of original image, hence the restoration of image details ensures the good quality of denoised image. Different from denoising the image as a whole, this paper proposes a novel denoising method that reconstructs the high and low frequency components respectively. The sparse representation using patch-based structure similarity is proposed to reconstruct the high frequency parts. And the low frequency parts are reconstructed by singular value decomposition (SVD). Finally an energy minimization function that contains high and low frequency parts are presented. Experimental results illustrate that the proposed method is outstanding in both numerical precision and visual performance. •The low and high frequency components are restored respectively.•A novel image denoising framework with SVD and sparse representation is proposed.•An energy function is proposed to aggregate the low and high frequency components.•Experiments show the competitiveness of the proposed method.