Feature pyramid serial attention network for underwater image enhancement

Underwater images often exhibit color distortion and low illumination because of the complex imaging mechanism of the underwater scene. These issues can significantly hinder the performance of underwater vision applications, including object segmentation and detection. To solve these limitations, we propose FPSANet, an innovative feature pyramid serial attention network designed for underwater image enhancement. This framework leverages multiscale feature fusion and an advanced attention mechanism. Initially, we propose a feature pyramid fusion module to integrate spatial information across multiple scales. Subsequently, we design a serial attention module (SAM) that prioritizes illumination features and emphasizes critical color details by combining with the pixel, channel, and space attention. Moreover, both qualitative analysis and quantitative evaluations reveal that our method excels across diverse underwater datasets, i.e., compared with the second-best comparative method, our method increases by 3.23% and 1.46%, at least in terms of the PSNR and SSIM values, respectively. The experimental results highlight its effectiveness in tasks such as image segmentation, keypoint detection, and even the enhancement of foggy images.