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.

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