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Owing to refraction, absorption, and scattering of light by suspendedparticles in water, raw underwater images suffer from low contrast, blurreddetails, and color distortion. These characteristics can significantlyinterfere with the visibility of underwater images and the result of visualtasks, such as segmentation and tracking. To address this problem, we propose anew robust adversarial learning framework via physics model based feedbackcontrol and domain adaptation mechanism for enhancing underwater images to getrealistic results. A new method for simulating underwater-like training datasetfrom RGB-D data by underwater image formation model is proposed. Upon thesynthetic dataset, a novel enhancement framework, which introduces a domainadaptive mechanism as well as a physics model constraint feedback control, istrained to enhance the underwater scenes. Final enhanced results on syntheticand real underwater images demonstrate the superiority of the proposed method,which outperforms nondeep and deep learning methods in both qualitative andquantitative evaluations. Furthermore, we perform an ablation study to show thecontributions of each component we proposed.

Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement