UIEVUS: An underwater image enhancement method for various underwater scenes

Abstract

Due to the scattering and absorption of light in water, underwater images commonly encounter degradation issues, such as color distortions and uneven brightness. To address these challenges, we introduce UIEVUS, an underwater image enhancement method designed for various underwater scenes. Building upon Retinex theory, our method implements an approach that combines Retinex decomposition with generative adversarial learning for targeted enhancement. The core innovation of UIEVUS lies in its ability to separately process and recover illumination and reflection maps before merging them into the final enhanced result. Specifically, the method first applies Retinex decomposition to separate the original underwater image into an illumination map (addressing uneven lighting) and a reflection map (addressing color distortion). The reflection map undergoes restoration through a lightweight encoder–decoder network that employs generative adversarial learning to recover color information. Concurrently, the illumination map receives enhancement guided by the reflection map, resulting in improved edges, details, brightness, and reduced noise. These enhanced components are then merged to produce the final result. Extensive experimental results demonstrate that UIEVUS achieves competitive performance against other comparative algorithms across various benchmark tests. Notably, our method strikes an optimal balance between computational efficiency and enhancement quality, making it suitable for practical UUV applications.