Multilevel Plug-and-Play Image Restoration
Résumé
Plug-and-play (PnP) image reconstruction methods leverage pretrained deep neural network denoisers as image priors to solve general inverse problems, and can obtain a competitive performance without having to train a network on a specific problem. Despite their flexibility, PnP methods often require several iterations to converge and their performance can be highly sensitive to the choice of the initialization and of the hyperparameters. In this paper, we propose a new multilevel PnP framework that accelerates the convergence by combining iterations at different scales, and improves the robustness to initialization and hyperparameters setting using a coarseto-fine strategy. In a series of experiments, including image inpainting and demosaicing, we show that the proposed multilevel PnP method outperforms other PnP methods in both speed and reconstruction performance.
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