Underwater Camera: Improving Visual Perception Via Adaptive Dark Pixel Prior and Color Correction

We present a novel method for underwater image restoration, which combines a Comprehensive Imaging Formation Model with prior knowledge and unsupervised techniques. Our approach has two main components: depth map estimation using a Channel Intensity Prior (CIP) and backscatter elimination through Adaptive Dark Pixels (ADP). The CIP effectively mitigates issues caused by solid-colored objects and highlighted regions in underwater scenarios. The ADP, utilizing a dynamic depth conversion, addresses issues associated with narrow depth ranges and backscatter. Furthermore, an unsupervised method is employed to enhance the accuracy of monocular depth estimation and reduce artificial illumination influence. The final output is refined via color compensation and a blue-green channel color balance factor, delivering artifact-free images. Experimental results show that our approach outperforms state-of-the-art methods, demonstrating its efficacy in dealing with uneven lighting and diverse underwater environments.