UADiff: A Deep Underwater Image Enhancement Network Using Generative Diffusion Prior and Uncertainty-Aware Learning

Diffusion models have provided the state-of-the-art performances for different computer vision tasks, including the task of underwater image enhancement. One of the challenges in the task of underwater image enhancement is that various spatial regions of the image require different restoration techniques. To address this, we propose a novel diffusion-based underwater image enhancement network, in which using the two ideas of uncertainty-aware learning (UAL) and feature recalibration based on the color tones dominated in the underwater environments, it is able to provide superior performances. Specifically, the former idea strives to process various spatial regions of the underwater image based on their restoration uncertainty, while the latter technique recalibrates the features generated by the diffusion model by taking the various color tones in the underwater environments into consideration. The results of different experimentations show the superiority of the proposed diffusion-based model over the other state-of-the-art underwater image enhancement networks.