Volume 11 - Volume 11
Image Denoising Using Low Rank Matrix Approximation in Singular Value Decomposition
Abstract
The factorization of a matrix into lower rank matrices give solutions to a wide range of computer
vision and image processing tasks. The inherent patches or the atomic patches can completely describe the whole image. The lower rank matrices are obtained using different tools including
Singular Value Decomposition (SVD), which is typically found in minimization problems of nuclear
norms. The singular values obtained will generally be a thresholder to realize the nuclear norm minimization. However, soft-thresholding is performed uniformly on all the singular values that lead
to a similar importance to all the patches whether it is principal/useful or not. Our observation is
that the decision on a patch (to be principal/useful or not) can be taken only when the application of
this minimization is taken into consideration. Thus, in this paper, we propose a new method for image denoising by choosing variable weights to different singular values with a deep noise effect. Experimental results illustrate that the proposed weighted scheme performs better than the state-of-the-art methods.
Paper Details
PaperID: 1769
Author's Name: V.V. Satyanarayana Tallapragada, G.V. Pradeep Kumar, D. Venkat Reddy and K.L. Narasihimhaprasad
Volume: Volume 11
Issues: Volume 11
Keywords: Image Denoising, Low Rank Matrix, Nuclear Norm, Singular Value, Soft-thresholding.
Year: 2021
Month: April
Pages: 1430-1446