A real-world underwater image enhancement method based on multi-color space and two-stage adaptive fusion

Due to the influence of light scattering, absorption and noise, the underwater environment presents a range of challenges for image processing tasks, such as color cast, low contrast and poor readability. It is truly regrettable that most algorithms primarily rely on synthetic datasets or few real-world river underwater images for evaluation. To address these challenges, we build a river underwater image enhancement dataset and propose an enhancement approach that leverages multi-color space, prior knowledge of underwater imaging and two-stage adaptive fusion. First, the shallow preprocessing module utilizes deformable convolution to better extract cross information. Next, the parallel RGB and HSV enhancement channels (with attention mechanism) can effectively highlight image characteristics in different colorspaces. Considering the underwater imaging prior, our method reduces the probability of artifacts. Finally, we merge the different frequency features in the first stage. The HSV and RGB components are fused adaptively in the second stage. Extensive experimental results verify the effectiveness of our algorithm in enhancing real-world underwater images.