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Journal Articles Inverse Problems Year : 2023

Block delayed Majorize-Minimize subspace algorithm for large scale image restoration

Abstract

Abstract In this work, we propose an asynchronous Majorization-Minimization (MM) algorithm for solving large scale differentiable non-convex optimization problems. The proposed algorithm runs efficient MM memory gradient updates on blocks of coordinates, in a parallel and possibly asynchronous manner. We establish the convergence of the resulting sequence of iterates under mild assumptions. The performance of the algorithm is illustrated on the restoration of 3D images degraded by depth-variant 3D blur, arising in multiphoton microscopy. Significant computational time reduction, scalability and robustness are observed on synthetic data, when compared to state-of-the-art methods. Experiments on the restoration of real acquisitions of a muscle structure illustrate the qualitative performance of our approach and its practical applicability.
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Dates and versions

hal-04281658 , version 1 (30-08-2022)
hal-04281658 , version 2 (07-01-2023)
hal-04281658 , version 3 (31-01-2023)
hal-04281658 , version 4 (13-11-2023)

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Mathieu Chalvidal, Emilie Chouzenoux, Jean-Baptiste Fest, Claire Lefort. Block delayed Majorize-Minimize subspace algorithm for large scale image restoration. Inverse Problems, 2023, Special Issue on Optimisation and Learning Methods for Inverse Problems in Microscopy, 39 (4), pp.044002. ⟨10.1088/1361-6420/acbdb9⟩. ⟨hal-04281658v4⟩
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