Adverse conditions typically suffer from stochastic hybrid weather degradations (e.g., rainy and hazy night), while existing image restoration algorithms envisage that weather degradations occur independently, thus may fail to handle real-world complicated scenarios. Besides, supervised training is not feasible due to the lack of comprehensive paired dataset to characterize hybrid conditions. To this end, we have advanced the forementioned limitations with two tactics: framework and data. On the one hand, we present a novel unified framework, dubbed RAHC, to Restore Arbitrary Hybrid adverse weather Conditions in one go, which can comfortably cope with hybrid scenarios with insufficient remaining background constituents and restore arbitrary hybrid conditions with a single trained model flexibly. On the other hand, we establish a new dataset, termed HAC, for learning and benchmarking arbitrary Hybrid Adverse Conditions restoration. HAC contains 31 scenarios composed of an arbitrary combination of five common weather, with a total of ~316K adverse-weather/clean pairs. As for fabrication, the training set is automatically generated by a dedicated AdverseGAN with no-frills labor, while the test set is manually modulated by experts for authoritative evaluation. Extensive experiments yield superior results and in particular establish new state-of-the-art results on both HAC and conventional datasets.

翻译：恶劣天气通常伴随随机混合的天气退化（例如，雨雾交加的夜晚），而现有图像恢复算法假定天气退化独立发生，因此可能无法处理现实中的复杂场景。此外，由于缺乏描述混合条件的全面配对数据集，监督训练不可行。为此，我们通过框架和数据两种策略推进了上述限制。一方面，我们提出了一种新的统一框架，名为RAHC，用于一次性恢复任意混合恶劣天气条件，该框架能够轻松应对背景成分残留不足的混合场景，并灵活地使用单一训练模型恢复任意混合条件。另一方面，我们建立了一个新的数据集，称为HAC，用于学习和评估任意混合恶劣条件的恢复。HAC包含由五种常见天气任意组合而成的31种场景，总计约31.6万对恶劣天气/清洁图像。在构建方面，训练集通过专用AdverseGAN自动生成，无需人工劳动，而测试集由专家手动调制以进行权威评估。大量实验得出了优异的结果，尤其在HAC和传统数据集上都建立了新的最先进水平。