TFCNet: A Hybrid Architecture for Multi-Task Restoration of Complex Underwater Optical Images

Underwater optical images are crucial in marine exploration. However, capturing these images directly often results in color distortion, noise, blurring, and other undesirable effects, all of which originate from the unique physical and chemical properties of underwater environments. Hence, various factors need to be comprehensively considered when processing underwater optical images that are severely degraded under complex lighting conditions. Most existing methods resolve one issue at a time, making it challenging for these isolated techniques to maintain consistency when addressing multiple degradation factors simultaneously, often leading to unsatisfactory visual outcomes. Motivated by the global modeling capability of the Transformer, this paper introduces TFCNet, a complex hybrid-architecture network designed for underwater optical image enhancement and restoration. TFCNet combines the benefits of the Transformer in capturing long-range dependencies with the local feature extraction potential of convolutional neural networks, resulting in enhanced restoration results. Compared with baseline methods, the proposed approach demonstrated consistent improvements, where it achieved minimum gains of 0.3 dB in the PSNR and 0.01 in the SSIM and a 0.8 reduction in the RMSE. TFCNet exhibited a commendable performance in complex underwater optical image enhancement and restoration tasks by effectively rectifying color distortion, eliminating marine snow noise to a certain degree, and restoring blur.