Perceptual underwater image enhancement with deep learning and physical priors

• URL: http://arxiv.org/abs/2008.09697v2
• Date: Sat, 26 Sep 2020 21:30:53 GMT
• Title: Perceptual underwater image enhancement with deep learning and physical priors
• Authors: Long Chen, Zheheng Jiang, Lei Tong, Zhihua Liu, Aite Zhao, Qianni Zhang, Junyu Dong, and Huiyu Zhou
• Abstract summary: We propose two perceptual enhancement models, each of which uses a deep enhancement model with a detection perceptor. Due to the lack of training data, a hybrid underwater image synthesis model, which fuses physical priors and data-driven cues, is proposed to synthesize training data. Experimental results show the superiority of our proposed method over several state-of-the-art methods on both real-world and synthetic underwater datasets.
• Score: 35.37760003463292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Underwater image enhancement, as a pre-processing step to improve the accuracy of the following object detection task, has drawn considerable attention in the field of underwater navigation and ocean exploration. However, most of the existing underwater image enhancement strategies tend to consider enhancement and detection as two independent modules with no interaction, and the practice of separate optimization does not always help the underwater object detection task. In this paper, we propose two perceptual enhancement models, each of which uses a deep enhancement model with a detection perceptor. The detection perceptor provides coherent information in the form of gradients to the enhancement model, guiding the enhancement model to generate patch level visually pleasing images or detection favourable images. In addition, due to the lack of training data, a hybrid underwater image synthesis model, which fuses physical priors and data-driven cues, is proposed to synthesize training data and generalise our enhancement model for real-world underwater images. Experimental results show the superiority of our proposed method over several state-of-the-art methods on both real-world and synthetic underwater datasets.

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