Domain Adaptation for Underwater Image Enhancement

• URL: http://arxiv.org/abs/2108.09650v1
• Date: Sun, 22 Aug 2021 06:38:19 GMT
• Title: Domain Adaptation for Underwater Image Enhancement
• Authors: Zhengyong Wang, Liquan Shen, Mei Yu, Kun Wang, Yufei Lin and Mai Xu
• Abstract summary: We propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to minimize the inter-domain and intra-domain gap. In the first phase, a new dual-alignment network is designed, including a translation part for enhancing realism of input images, followed by an enhancement part. In the second phase, we perform an easy-hard classification of real data according to the assessed quality of enhanced images, where a rank-based underwater quality assessment method is embedded.
• Score: 51.71570701102219
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recently, learning-based algorithms have shown impressive performance in underwater image enhancement. Most of them resort to training on synthetic data and achieve outstanding performance. However, these methods ignore the significant domain gap between the synthetic and real data (i.e., interdomain gap), and thus the models trained on synthetic data often fail to generalize well to real underwater scenarios. Furthermore, the complex and changeable underwater environment also causes a great distribution gap among the real data itself (i.e., intra-domain gap). However, almost no research focuses on this problem and thus their techniques often produce visually unpleasing artifacts and color distortions on various real images. Motivated by these observations, we propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to simultaneously minimize the inter-domain and intra-domain gap. Concretely, a new dual-alignment network is designed in the first phase, including a translation part for enhancing realism of input images, followed by an enhancement part. With performing image-level and feature-level adaptation in two parts by jointly adversarial learning, the network can better build invariance across domains and thus bridge the inter-domain gap. In the second phase, we perform an easy-hard classification of real data according to the assessed quality of enhanced images, where a rank-based underwater quality assessment method is embedded. By leveraging implicit quality information learned from rankings, this method can more accurately assess the perceptual quality of enhanced images. Using pseudo labels from the easy part, an easy-hard adaptation technique is then conducted to effectively decrease the intra-domain gap between easy and hard samples.

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