Two-Branch Underwater Image Enhancement and Original Resolution Information Optimization Strategy in Ocean Observation

In complex marine environments, underwater images often suffer from color distortion, blur, and poor visibility. Existing underwater image enhancement methods predominantly rely on the U-net structure, which assigns the same weight to different resolution information. However, this approach lacks the ability to extract sufficient detailed information, resulting in problems such as blurred details and color distortion. We propose a two-branch underwater image enhancement method with an optimized original resolution information strategy to address this limitation. Our method comprises a feature enhancement subnetwork (FEnet) and an original resolution subnetwork (ORSnet). FEnet extracts multi-resolution information and utilizes an adaptive feature selection module to enhance global features in different dimensions. The enhanced features are then fed into ORSnet as complementary features, which extract local enhancement features at the original image scale to achieve semantically consistent and visually superior enhancement effects. Experimental results on the UIEB dataset demonstrate that our method achieves the best performance compared to the state-of-the-art methods. Furthermore, through comprehensive application testing, we have validated the superiority of our proposed method in feature extraction and enhancement compared to other end-to-end underwater image enhancement methods.