Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement
This addresses visibility issues in underwater images for applications like segmentation and tracking, but it is incremental as it builds on existing adversarial learning and domain adaptation techniques.
The paper tackles the problem of low contrast, blurred details, and color distortion in underwater images by proposing a domain adaptive adversarial learning framework with physics model feedback, which outperforms existing methods in qualitative and quantitative evaluations.
Owing to refraction, absorption, and scattering of light by suspended particles in water, raw underwater images suffer from low contrast, blurred details, and color distortion. These characteristics can significantly interfere with the visibility of underwater images and the result of visual tasks, such as segmentation and tracking. To address this problem, we propose a new robust adversarial learning framework via physics model based feedback control and domain adaptation mechanism for enhancing underwater images to get realistic results. A new method for simulating underwater-like training dataset from RGB-D data by underwater image formation model is proposed. Upon the synthetic dataset, a novel enhancement framework, which introduces a domain adaptive mechanism as well as a physics model constraint feedback control, is trained to enhance the underwater scenes. Final enhanced results on synthetic and real underwater images demonstrate the superiority of the proposed method, which outperforms nondeep and deep learning methods in both qualitative and quantitative evaluations. Furthermore, we perform an ablation study to show the contributions of each component we proposed.