Single Underwater Image Restoration by Contrastive Learning

• URL: http://arxiv.org/abs/2103.09697v1
• Date: Wed, 17 Mar 2021 14:47:03 GMT
• Title: Single Underwater Image Restoration by Contrastive Learning
• Authors: Junlin Han and Mehrdad Shoeiby and Tim Malthus and Elizabeth Botha and Janet Anstee and Saeed Anwar and Ran Wei and Lars Petersson and Mohammad Ali Armin
• Abstract summary: This paper elaborates on a novel method that achieves state-of-the-art results for underwater image restoration based on the unsupervised image-to-image translation framework. We design our method by leveraging from contrastive learning and generative adversarial networks to maximize mutual information between raw and restored images.
• Score: 23.500647404118638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Underwater image restoration attracts significant attention due to its importance in unveiling the underwater world. This paper elaborates on a novel method that achieves state-of-the-art results for underwater image restoration based on the unsupervised image-to-image translation framework. We design our method by leveraging from contrastive learning and generative adversarial networks to maximize mutual information between raw and restored images. Additionally, we release a large-scale real underwater image dataset to support both paired and unpaired training modules. Extensive experiments with comparisons to recent approaches further demonstrate the superiority of our proposed method.

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