Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network

Sijin Kim, Namhyuk Ahn, Kyung-Ah Sohn; Proceedings of the Asian Conference on Computer Vision (ACCV), 2020

Abstract


In recent years, deep learning-based methods have been successfully applied to the image distortion restoration tasks.However, scenarios that assume a single distortion only may not be suitable for many real-world applications.To deal with such cases, some studies have proposed sequentially combined distortions datasets.Viewing in a different point of combining, we introduce a spatially-heterogeneous distortion dataset in which multiple corruptions are applied to the different locations of each image.In addition, we also propose a mixture of experts network to effectively restore a multi-distortion image.Motivated by the multi-task learning, we design our network to have multiple paths that learn both common and distortion-specific representations.Our model is effective for restoring real-world distortions and we experimentally verify that our method outperforms other models designed to manage both single distortion and multiple distortions.

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[bibtex]
@InProceedings{Kim_2020_ACCV, author = {Kim, Sijin and Ahn, Namhyuk and Sohn, Kyung-Ah}, title = {Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {November}, year = {2020} }