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Generalized Sparse Variation Regularization for Large Fluorescence Image Deconvolution

Abstract : In this work, we generalize the sparse variation (SV) combining the total-variation (TV) and the L 1 regularization and introduce a novel family of convex and non-quadratic regularizers for fast deconvolution of large 2D fluorescence images. These regularizers are defined as mixed Lp-L 2 norms (p ≥ 1) which group image intensity and spatial differentials, computed at each pixel of the image. By coupling a regularization term of this family with a quadratic data fidelity term, we propose a fast and efficient deconvolution method by using the primal-dual (proximal) algorithms to minimize the corresponding energy functional. Experiment results on both 2D simulated and real fluorescence scanner images demonstrate the performance of our method in terms of restoration quality as well as computational time.
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Contributor : Charles Kervrann Connect in order to contact the contributor
Submitted on : Wednesday, October 4, 2017 - 9:35:29 AM
Last modification on : Wednesday, December 23, 2020 - 5:28:03 PM


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  • HAL Id : hal-01609810, version 1



Hoaï-Nam Nguyen, Vincent Paveau, Cyril Cauchois, Charles Kervrann. Generalized Sparse Variation Regularization for Large Fluorescence Image Deconvolution. 2017. ⟨hal-01609810⟩



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