Two-Stage Spatial-Detail Underwater Image Enhancement Method Based on Semantic Labels

Underwater image enhancement (UIE) can improve the effectiveness of applications, such as underwater object detection. In UIE, synthetic images or pseudoimages are commonly used as enhancement references. However, the synthetic images cannot simulate underwater scenes, and there is interdomain difference between synthetic images and the underwater environment. Since different enhancement methods produce different effects on different semantic regions of the same image, pseudolabels lose valuable information from other enhancement methods. Moreover, different semantic information has different requirements for enhanced effectiveness. If the enhancement reference is provided on the semantic scale instead of the image scale, the advantages of various enhancement methods will be better utilized. To achieve this, this article introduces a method of semantic labels, which uses the results of different enhancement methods to label different semantic regions of the same image to build a more comprehensive reference. At the same time, enhancement images are often restricted by insufficient texture detail in advanced applications. Existing UIE methods employ the encoder–decoder architecture, which is effective in encoding broad contextual information but not reliable in preserving spatial image details. To simultaneously capture spatial information and retain image details, a two-stage spatial and detail network is proposed, which integrates U-Net and original resolution network to enhance the network’s ability to retain details. The experimental results show that the proposed method has better qualitative and quantitative evaluation results than the existing methods. Ablation studies demonstrate the effectiveness of the various components.