A Pixel Distribution Remapping and Multi-Prior Retinex Variational Model for Underwater Image Enhancement

High-quality underwater imaging is crucial for underwater exploration. However, particle scattering and light absorption by seawater significantly degrade image clarity. To address these issues, we propose a novel underwater image enhancement (UIE) method that combines pixel distribution remapping (PDR) with a multi-priority Retinex variational model. We design a pre-compensation method for severely attenuated channels that effectively prevents new color artifacts during color correction. By combining the inter-channel coupling relationships, we compute a limiting factor to remap pixel distribution curves to improve image contrast. In addition, considering the significant noise interference, we introduce the prior knowledge, including underwater noise and texture priors, while constructing the variational model, and design penalty terms that match the underwater characteristics to remove excessive noise in the reflectance component. Our approach efficiently decouples the illumination and reflectance components using a rapid solver. Subsequently, gamma correction adjusts the illumination component, and the corrected illumination and reflectance components are fused to reconstruct the final natural output image. Comprehensive evaluations across various datasets reveal that our approach significantly surpasses current state-of-the-art (SOTA) methods. These results demonstrate the effectiveness of our method in correcting color bias and compensating for luminance losses in underwater imagery. Our code is available at: https://github.com/zhoujingchun03/PDRMRV .