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Underwater image enhancement, as a pre-processing step to improve theaccuracy of the following object detection task, has drawn considerableattention in the field of underwater navigation and ocean exploration. However,most of the existing underwater image enhancement strategies tend to considerenhancement and detection as two independent modules with no interaction, andthe practice of separate optimization does not always help the underwaterobject detection task. In this paper, we propose two perceptual enhancementmodels, each of which uses a deep enhancement model with a detection perceptor.The detection perceptor provides coherent information in the form of gradientsto the enhancement model, guiding the enhancement model to generate patch levelvisually pleasing images or detection favourable images. In addition, due tothe lack of training data, a hybrid underwater image synthesis model, whichfuses physical priors and data-driven cues, is proposed to synthesize trainingdata and generalise our enhancement model for real-world underwater images.Experimental results show the superiority of our proposed method over severalstate-of-the-art methods on both real-world and synthetic underwater datasets.

Perceptual underwater image enhancement with deep learning and physical priors