MFP-PAINet: Enhancing Underwater Images Through Multi-Dimensional Feature Fusion and Probabilistic Uncertainty Modeling

In supervised training for underwater image enhancement (UIE), many deep learning methods rely on approximate reference images; however, they tend to neglect the inherent uncertainty of these references. To address this problem, this study proposes MFP-PAINet, an underwater image enhancement network, which can integrate multi-dimensional feature fusion with probabilistic adaptive uncertainty modeling. Specifically, histogram equalization, white balance correction, and gamma correction were first implemented to generate preliminary enhanced inputs so as to improve contrast and color balance. Subsequently, a multi-dimensional feature mapping module was used to extract high-dimensional representations and produce multiple feature maps, which were dynamically weighted and adaptively fused by a confidence generation module to initially handle the uncertainty at the feature level. Finally, a probabilistic module was introduced to further model the uncertainty and refine image details. Extensive experiments conducted on eight publicly available datasets demonstrate that the proposed MFP-PAINet outperforms both traditional and deep learning-based UIE methods. For example, on the LSUI dataset, MFP-PAINet achieved SSIM, PSNR, and UIQM scores of 0.854, 23.044, and 3.056, respectively. Furthermore, MFP-PAINet exhibited certain promising performance in image deraining and image dehazing tasks, proving that it can effectively retrieve image details and improve image clarity.