Wavelet-pixel domain progressive fusion network for underwater image enhancement

Due to the complexity of the underwater environment, underwater images suffer from low-illumination, color deviation, and blurred details. Conventional underwater image enhancement (UIE) approaches have achieved remarkable success in improving color and illumination in the pixel domain. However, they ignore fine-grained details during the enhancement process. To solve these problems, we propose a novel wavelet-pixel domain progressive fusion network (WPFNet) to improve details while enhancing illumination and reducing color deviation for degraded underwater images. Unlike the UIE method, we propose a wavelet domain module (WDM) to obtain different scale frequency features with fine-grained details. Concretely, we develop a residual attention block (RAB) that optimizes low-frequency sub-images to maintain rich details, and enhance color and illumination information for low-frequency features.Transformer block (T_Block) is proposed to convert three high-frequency sub-images into high-frequency features operated by self-attention mechanism. Frequency features are obtained by adding low- and high-frequency features from the same scale, suffering from insufficient color and illumination information. Thus, the pixel domain module (PDM) is introduced to extract spatial features of different scales with rich color and illumination information in the pixel domain. Reconstruction module (REM) is proposed to fuse frequency and spatial features from small to large scale to restructure clear underwater images. The dual-domain fusion block (DFB) is proposed as a key element of REM, which exploits the constraint signal to fuse superior information of frequency and spatial features. Finally, our WPFNet obtains high-quality underwater images in four benchmark datasets and achieves superior performance compared to state-of-the-art UIE methods. The code is available at: https://github.com/LiuShiBen/WPFNet.