Degradation-Agnostic Clarity Learning for Unpaired Image Dehazing

Unpaired image dehazing models cast the restoration process as unsupervised domain translation, effectively avoiding the rigid physical assumptions of traditional methods. However, these methods frequently suffer from severe semantic hallucinations and content distortion. This stems from the significant semantic gap between unpaired clean and hazy domains, forcing the discriminator to overfit to high-level semantics rather than low-level clarity. In this paper, we propose Degradation-Agnostic Clarity Learning (DCL), a novel paradigm that steers the network to learn genuine low-level clarity. We introduce two core insights: (1) Explicitly injecting low-level degradations provides strong surface statistical shortcuts that effectively suppress high-level semantic interference; (2) Clarity is a relative concept, and various degraded variants of a clean image share a common intrinsic clarity direction. To realize this, we innovatively formulate discriminator training as a Multi-Task Learning (MTL) problem. First, we employ Adversarial AutoAugment to dynamically mine unlearned, diverse degraded clean samples by maximizing the discriminator’s loss. Second, we utilize Pareto Optimization to extract the shared common descent direction across degraded variants, thereby preventing the model from overfitting to any specific degradation type. Furthermore, we extend our core hypothesis to an Enhance-to-Resist self-supervised texture enhancement paradigm that benefits both dehazing and lowlight enhancement tasks. Extensive experiments on real-world datasets demonstrate that DCL significantly suppresses semantic hallucinations and achieves state-of-the-art performance in unpaired dehazing.