Reinforced Res-Unet transformer for underwater image enhancement

Light propagation through water is subject to varying degrees of energy loss, causing captured images to display characteristics of color distortion, reduced contrast, and indistinct details and textures. The data-driven approach offers significant advantages over traditional algorithms, such as improved accuracy and reduced computational costs. However, challenges such as optimizing network architecture, refining coding techniques, and expanding database resources must be addressed to ensure the generation of high-quality reconstructed images across diverse tasks. In this paper, an underwater image enhancement network based on feature fusion is proposed named RUTUIE, which integrates feature fusion techniques. It leverages the strengths of both Resnet and U-shape architecture, primarily structured around a streamlined up-and-down sampling mechanism. Specifically, the U-shaped structure serves as the backbone of ResNet, equipped with two feature transformers at both the encoding and decoding ends, which are linked by a single-stage up-and-down sampling structure. This architecture is designed to minimize the omission of minor features during feature scale transformations. Furthermore, the improved Transformer encoder leverages a feature-level attention mechanism and the advantages of CNNs, endowing the network with both local and global perceptual capabilities. Then, we propose and demonstrate that embedding an adaptive feature selection module at appropriate locations can retain more learned feature representations. Moreover, the application of a previously proposed color transfer method for synthesizing underwater images and augmenting network training. Extensive experiments demonstrate that our work effectively corrects color casts, reconstructs the rich texture information in natural scenes, and improves the contrast.