DMS-CycleGAN: an effective underwater image enhancement method

Abstract Underwater image enhancement is a critical component of underwater vision technology and has attracted growing attention from researchers worldwide. However, adverse factors such as excessive noise, severe haziness, and color distortion—primarily caused by the scattering and absorption of light in water as well as suspended particle interference—make it difficult to attain reliable enhancement of underwater images. Although conventional enhancement methods can improve image quality to some extent, they still face limitations in addressing these issues. To overcome such challenges, this article proposes an effective CycleGAN-based underwater image enhancement method, termed Dark-channel Multi-head Smooth CycleGAN (DMS-CycleGAN). Unlike previous studies that adopt attention mechanisms, activation functions, or loss terms individually, our method systematically integrates four complementary components:(1) a Dark Channel Prior (DCP) refined with guided filtering to deliver clearer and more stable input data; (2) multi-head attention modules integrated into the generator to effectively capture spatially variant degradation and enhance fine-grained details; (3) the Mish activation function in the discriminator to stabilize adversarial training and suppress artifacts; and (4) a texture loss function designed to preserve local structures and details detail realism beyond conventional perceptual or structural similarity losses. This unified design is developed to address challenges specific to underwater imagery, with the goal of systematically improving color fidelity, structural consistency, and texture preservation. Comprehensive experiments conducted on benchmark datasets demonstrate that the proposed DMS-CycleGAN outperforms existing approaches in both subjective and objective evaluations, particularly in terms of color cast correction and detail retention.