Multi-core token mixer: a novel approach for underwater image enhancement

Tianrun Xu, Shiyuan Xu, Xue Chen, Feng Chen, Hongjue Li

Abstract: Underwater image enhancement (UIE) is critical in various applications, including marine biology research, underwater archaeology, and autonomous underwater vehicle (AUV) navigation. The unpredictable nature of underwater environments frequently leads to degradation in contrast, color, and perceptual visual quality. Previous methods using the single receptive field to extract features are not capable of handling varying light conditions, which hinders detail preservation, color correction, and image quality improvement. To address these challenges, we propose Multi Core Token Mixer (MCTM) by introducing a distinctive multi-core mechanism. This mechanism is adept at extracting varied receptive fields, thereby enabling the model to capture the degradation at different scales caused by inhomogeneous underwater conditions. We performed experiments on three datasets (UIEB, EUVP, and UFO-120), and MCTM consistently outperforms existing models in image enhancement, color correction, and perceptual visual quality. Our work sets a new standard in the field and emphasizes the promise held by task-specific architectures that harness the power of Transformer models to tackle domain-specific challenges, particularly in UIE.