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• W2142224912 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." @default.
• W2142224912 created "2016-06-24" @default.
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• W2142224912 date "2009-11-01" @default.
• W2142224912 modified "2023-10-14" @default.
• W2142224912 title "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems" @default.
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• W2142224912 doi "https://doi.org/10.1109/tip.2009.2028250" @default.
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