Underwater image enhancement via color constraints and transmission-guided modeling

Abstract

Underwater images suffer from color deviation and turbidity due to absorption and refraction caused by media. To alleviate severe degradation, we devise an underwater image enhancement method via color constraints and transmission-guided modeling, dubbed CTGAN. Specifically, the critical insight of CTGAN is to break down the overall enhancement process into more manageable steps, thereby enjoying the mutual benefits between color correction and turbidity removal. We develop an interactive constraint color recovery module, which integrates the mean value and mode priors of color channels to render the realistic color. Coupled with a transmission-guided strategy, turbidity traces are gracefully eliminated by integrating heterogeneous degradation cues. To bridge the gap between enhanced and reference images, a frequency-driven triple discriminator is implemented to guide the generation of visually pleasing appearances. We also contribute an Underwater Image Visual Perceptual Enhancement Benchmark (UVPE) to support qualitative and quantitative analysis. Extensive experiments demonstrate the superiority of CTGAN against state-of-the-art methods.