Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network
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 combin...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
30.09.2020
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Subjects | |
Online Access | Get full text |
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Summary: | 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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DOI: | 10.48550/arxiv.2009.14563 |