The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Image Denoising Using the Higher Order Singular Value Decomposition
Abstract
In this paper, we propose a very simple and elegant patch-based, machine learning technique for image denoising using the higher order singular value decomposition (HOSVD). The technique simply groups together similar patches from a noisy image (with similarity defined by a statistically motivated criterion) into a 3D stack, computes the HOSVD coefficients of this stack, manipulates these coefficients by hard thresholding, and inverts the HOSVD transform to produce the final filtered image. Our technique chooses all required parameters in a principled way, relating them to the noise model. We also discuss our motivation for adopting the HOSVD as an appropriate transform for image denoising. We experimentally demonstrate the excellent performance of the technique on grayscale as well as color images. On color images, our method produces state-of-the-art results, outperforming other color image denoising algorithms at moderately high noise levels. A criterion for optimal patch-size selection and noise variance estimation from the residual images (after denoising) is also presented.
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Cites
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Research works printed but not linked: 5
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- 10.2307/2337118 · DOI
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Cited by
Learning Deep CNN Denoiser Prior for Image Restoration
Zhang, K., Zuo, W., Gu, S., & Zhang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.300 · Cited by 2,116
Chen, Y., & Pock, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2596743 · Cited by 1,344
Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations
Zhuang, L., & Bioucas-Dias, J. M. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2796570 · Cited by 363
A review on medical image denoising algorithms
Mohd Sagheer, S. V., & George, S. N. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102036 · Cited by 316
Bearing fault diagnosis based on SVD feature extraction and transfer learning classification
Shen, F., Chen, C., Yan, R., & Gao, R. X. (2015) · 2015 Prognostics and System Health Management Conference (PHM) · doi:10.1109/phm.2015.7380088 · Cited by 119
Metadata
- Authors
- Rajwade, A., Rangarajan, A., & Banerjee, A.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2013-04
- Volume
- 35
- Issue
- 4
- Pages
- 849-862
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2154011501
- Cited by
- 375
- References
- 43
- FWCI
- 21.97
- Type
- journal article
- Language
- en
- Open access
- closed
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- Rajwade, A., Rangarajan, A., & Banerjee, A. (2013). Image Denoising Using the Higher Order Singular Value Decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2012.140
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