Image Denoising Using Block-Rotation-Based SVD Filtering in Wavelet Domain
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.6 pp.1621-1628
- Publication Date
- 2018/06/01
- Publicized
- 2018/03/14
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDP7055
- Type of Manuscript
- PAPER
- Category
- Image Processing and Video Processing
Authors
Min WANG
National University of Defense Technology
Shudao ZHOU
National University of Defense Technology
Keyword
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Min WANG, Shudao ZHOU, "Image Denoising Using Block-Rotation-Based SVD Filtering in Wavelet Domain" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 6, pp. 1621-1628, June 2018, doi: 10.1587/transinf.2018EDP7055.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7055/_p
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@ARTICLE{e101-d_6_1621,
author={Min WANG, Shudao ZHOU, },
journal={IEICE TRANSACTIONS on Information},
title={Image Denoising Using Block-Rotation-Based SVD Filtering in Wavelet Domain},
year={2018},
volume={E101-D},
number={6},
pages={1621-1628},
abstract={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.},
keywords={},
doi={10.1587/transinf.2018EDP7055},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - Image Denoising Using Block-Rotation-Based SVD Filtering in Wavelet Domain
T2 - IEICE TRANSACTIONS on Information
SP - 1621
EP - 1628
AU - Min WANG
AU - Shudao ZHOU
PY - 2018
DO - 10.1587/transinf.2018EDP7055
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2018
AB - 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.
ER -
English
