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• W3090791806 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." @default.
• W3090791806 created "2020-10-08" @default.
• W3090791806 creator A5047656074 @default.
• W3090791806 creator A5062357712 @default.
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• W3090791806 date "2020-09-30" @default.
• W3090791806 modified "2023-10-16" @default.
• W3090791806 title "Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network" @default.
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• W3090791806 doi "https://doi.org/10.48550/arxiv.2009.14563" @default.
• W3090791806 hasPublicationYear "2020" @default.
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