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arXiv · 2009.14563

Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network

Sijin Kim·Namhyuk Ahn·Kyung-Ah Sohn
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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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Sijin Kim, Namhyuk Ahn, Kyung-Ah Sohn. 2020-09-30. Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network. https://arxiv.org/abs/2009.14563

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