Learning From the Abyss: Self-Adaptive Distillation for Enhancing Underwater Images With Complex Degradations

Abstract

While deep learning has advanced underwater image enhancement (UIE), progress is fundamentally hampered by the scarcity of paired training data. Semi-supervised methods, which leverage abundant unlabeled images, offer a promising solution but often employ naive, static weighting for pseudo-labels. This overlooks a key principle: the most informative training samples are those that represent a successful restoration from severe degradation, thus possessing a greater transformation difficulty. To address this, we propose a novel adaptive training paradigm, the Self-Distilled Degradation-Aware Network (SDDA-Net), governed by an incentive-driven weighting mechanism. We introduce a scoring function that evaluates each pseudo-label by considering both its perceptual quality and a term that is inversely proportional to its structural similarity to the input. This score, which quantifies the difficulty and success of the enhancement, is then used to dynamically modulate the sample’s influence on the unsupervised loss. By allocating greater learning weights to successfully restored, complex examples, our framework forces the model to focus on mastering challenging degradations. Extensive experiments on multiple benchmark datasets demonstrate that this adaptive approach significantly outperforms current state-of-the-art methods, validating that the strategic, sample-adaptive modulation of the learning process is critical for success in data-scarce restoration tasks.