Image Dehazing (ID) aims to produce a clear image from an observation contaminated by haze. Current ID methods typically rely on carefully crafted priors or extensive haze-free ground truth, both of which are expensive or impractical to acquire, particularly in the context of scientific imaging. We propose a new unsupervised learning framework called Equivariant Image Dehazing (EID) that exploits the symmetry of image signals to restore clarity to hazy observations. By enforcing haze consistency and systematic equivariance, EID can recover clear patterns directly from raw, hazy images. Additionally, we propose an adversarial learning strategy to model unknown haze physics and facilitate EID learning. Experiments on two scientific image dehazing benchmarks (including cell microscopy and medical endoscopy) and on natural image dehazing have demonstrated that EID significantly outperforms state-of-the-art approaches. By unifying equivariant learning with modelling haze physics, we hope that EID will enable more versatile and effective haze removal in scientific imaging. Code and datasets will be published.

翻译：图像去雾旨在从受雾霾污染的观测图像中恢复清晰图像。现有去雾方法通常依赖于精心设计的先验知识或大量无雾真实数据，这两者在获取上均成本高昂或难以实现，尤其在科学成像领域。本文提出一种名为等变图像去雾的新型无监督学习框架，该框架利用图像信号的对称性来恢复模糊观测的清晰度。通过强制雾霾一致性与系统等变性，EID能够直接从原始模糊图像中恢复清晰图案。此外，我们提出一种对抗性学习策略来建模未知雾霾物理机制并促进EID学习。在两个科学图像去雾基准数据集（包括细胞显微镜与医学内窥镜图像）及自然图像去雾任务上的实验表明，EID显著优于现有先进方法。通过将等变学习与雾霾物理建模相统一，我们希望EID能够在科学成像领域实现更通用高效的去雾处理。代码与数据集将予以公开。