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Adverse conditions typically suffer from stochastic hybrid weatherdegradations (e.g., rainy and hazy night), while existing image restorationalgorithms envisage that weather degradations occur independently, thus mayfail to handle real-world complicated scenarios. Besides, supervised trainingis not feasible due to the lack of a comprehensive paired dataset tocharacterize hybrid conditions. To this end, we have advanced theaforementioned limitations with two tactics: framework and data. First, wepresent a novel unified framework, dubbed RAHC, to Restore Arbitrary Hybridadverse weather Conditions in one go. Specifically, our RAHC leverages amulti-head aggregation architecture to learn multiple degradationrepresentation subspaces and then constrains the network to flexibly handlemultiple hybrid adverse weather in a unified paradigm through a discriminationmechanism in the output space. Furthermore, we devise a reconstruction vectorsaided scheme to provide auxiliary visual content cues for reconstruction, thuscan comfortably cope with hybrid scenarios with insufficient remaining imageconstituents. Second, we construct a new dataset, termed HAC, for learning andbenchmarking arbitrary Hybrid Adverse Conditions restoration. HAC contains 31scenarios composed of an arbitrary combination of five common weather, with atotal of ~316K adverse-weather/clean pairs. Extensive experiments yieldsuperior results and establish new state-of-the-art results on both HAC andconventional datasets.

Restoring Images Captured in Arbitrary Hybrid Adverse Weather Conditions in One Go