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• W2962912897 abstract "Total variation (TV) is a widely used regularizer for stabilizing the solution of ill-posed inverse problems. In this paper, we propose a novel proximal-gradient algorithm for minimizing TV regularized least-squares cost functional. Our method replaces the standard proximal step of TV by a simpler alternative that computes several independent proximals. We prove that the proposed parallel proximal method converges to the TV solution, while requiring no sub-iterations. The results in this paper could enhance the applicability of TV for solving very large scale imaging inverse problems." @default.
• W2962912897 created "2019-07-30" @default.
• W2962912897 creator A5024602237 @default.
• W2962912897 date "2016-03-01" @default.
• W2962912897 modified "2023-09-26" @default.
• W2962912897 title "Parallel proximal methods for total variation minimization" @default.
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• W2962912897 doi "https://doi.org/10.1109/icassp.2016.7472568" @default.
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