The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
Abstract
This paper studies gradient-based schemes for image denoising and deblurring problems based on the discretized total variation (TV) minimization model with constraints. We derive a fast algorithm for the constrained TV-based image deburring problem. To achieve this task, we combine an acceleration of the well known dual approach to the denoising problem with a novel monotone version of a fast iterative shrinkage/thresholding algorithm (FISTA) we have recently introduced. The resulting gradient-based algorithm shares a remarkable simplicity together with a proven global rate of convergence which is significantly better than currently known gradient projections-based methods. Our results are applicable to both the anisotropic and isotropic discretized TV functionals. Initial numerical results demonstrate the viability and efficiency of the proposed algorithms on image deblurring problems with box constraints.
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Citations
Cites
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Beck, A., & Teboulle, M. (2009) · SIAM Journal on Imaging Sciences · doi:10.1137/080716542 · Cited by 11,952
Self-citationA New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration
Bioucas-Dias, J. M., & Figueiredo, M. A. T. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.909319 · Cited by 2,064
Cited by
Nonlocally Centralized Sparse Representation for Image Restoration
Dong, W., Zhang, L., Shi, G., & Li, X. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2235847 · Cited by 1,505
Extreme Learning Machine for Multilayer Perceptron
Tang, J., Deng, C., & Huang, G.-B. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2424995 · Cited by 1,451
The Little Engine That Could: Regularization by Denoising (RED)
Romano, Y., Elad, M., & Milanfar, P. (2017) · SIAM Journal on Imaging Sciences · doi:10.1137/16m1102884 · Cited by 934
Computer vision-based concrete crack detection using U-net fully convolutional networks
Liu, Z., Cao, Y., Wang, Y., & Wang, W. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.04.005 · Cited by 833
Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration
He, W., Zhang, H., Zhang, L., & Shen, H. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2452812 · Cited by 667
Metadata
- Authors
- Beck, A., & Teboulle, M.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2009-11
- Volume
- 18
- Issue
- 11
- Pages
- 2419-2434
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2142224912
- Cited by
- 2,004
- References
- 2
- FWCI
- 71.46
- Type
- journal article
- Language
- en
- Open access
- closed
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- Beck, A., & Teboulle, M. (2009). Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2009.2028250
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