Real Underwater Image Restoration From a Unified Perspective

The fidelity of underwater visual data is significantly affected by various factors, encompassing light refraction and color distortion in water. These factors often give rise to noise and distortion within underwater images. In response to the widespread nature of this issue, we propose a novel approach to restore authentic underwater images through a comprehensive perspective. To address the inherent scarcity of real underwater image datasets, we introduce an innovative underwater image generation model that leverages Generative Adversarial Networks (GAN) with integrated physical constraints, aiming to produce realistic and stable underwater image datasets. Within the underwater image restoration framework, we incorporate a multi-channel feature extractor (MCFE) module, which is designed to enhance the model’s capability in image feature extraction. Additionally, we introduce object edge information as a novel loss function to perform different repairs on the target and background. Experimental evaluations demonstrate that our method exhibits superior performance in both qualitative and quantitative assessments compared to state-of-the-art approaches. Restoration results on real underwater images further showcase its exceptional performance in practical applications. The source code and sample dataset are publicly available at here.