An unsupervised underwater image enhancement method based on generative adversarial networks with edge extraction

Underwater environments pose significant challenges for image capture due to factors like light absorption, scattering, and the presence of particles in the water. These factors degrade the quality of underwater images, impacting tasks like target detection and recognition. The challenge with deep learning-based underwater image enhancement methods is their reliance on paired datasets, which consist of degraded and corresponding ground-truth images. Obtaining such paired datasets in natural conditions is challenging, leading to performance issues in these algorithms. To address this issue, we propose an unsupervised generative adversarial network with edge detection for enhancing underwater images without needing paired data. First, we introduce the perceptual loss function into the conventional loss function to better measure the performance of two generative networks. Second, we propose an edge extraction block based on the Laplacian operator, an attention module with an edge extraction block, a multi-scale feature module, a novel upsampling module, and a new downsampling module. We use these proposed modules to design a new generative network. Third, we use the proposed multi-scale feature and downsampling modules to design the adversarial network. We tested the algorithm’s performance on both synthetic and authentic underwater images. Compared to existing state-of-the-art methods, our proposed approach better enhances image details and restores color information.