Image Denoising Using Block-Rotation-Based SVD Filtering in Wavelet Domain

Min WANG  Shudao ZHOU  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.6   pp.1621-1628
Publication Date: 2018/06/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7055
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
image denoising,  threshold denoising,  singular value decomposition,  peak signal-to-noise ratio,  structural similarity index,  

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This paper proposes an image denoising method using singular value decomposition (SVD) with block-rotation-based operations in wavelet domain. First, we decompose a noisy image to some sub-blocks, and use the single-level discrete 2-D wavelet transform to decompose each sub-block into the low-frequency image part and the high-frequency parts. Then, we use SVD and rotation-based SVD with the rank-1 approximation to filter the noise of the different high-frequency parts, and get the denoised sub-blocks. Finally, we reconstruct the sub-block from the low-frequency part and the filtered the high-frequency parts by the inverse wavelet transform, and reorganize each denoised sub-blocks to obtain the final denoised image. Experiments show the effectiveness of this method, compared with relevant methods.