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Images acquired from rainy scenes usually suffer from bad visibility whichmay damage the performance of computer vision applications. The rainy scenarioscan be categorized into two classes: moderate rain and heavy rain scenes.Moderate rain scene mainly consists of rain streaks while heavy rain scenecontains both rain streaks and the veiling effect (similar to haze). Althoughexisting methods have achieved excellent performance on these two casesindividually, it still lacks a general architecture to address both heavy rainand moderate rain scenarios effectively. In this paper, we construct ahierarchical multi-direction representation network by using the contourlettransform (CT) to address both moderate rain and heavy rain scenarios. The CTdivides the image into the multi-direction subbands (MS) and the semanticsubband (SS). First, the rain streak information is retrieved to the MS basedon the multi-orientation property of the CT. Second, a hierarchicalarchitecture is proposed to reconstruct the background information includingdamaged semantic information and the veiling effect in the SS. Last, themulti-level subband discriminator with the feedback error map is proposed. Bythis module, all subbands can be well optimized. This is the first architecturethat can address both of the two scenarios effectively. The code is availablein https://github.com/cctakaet/ContourletNet-BMVC2021.

ContourletNet: A Generalized Rain Removal Architecture Using Multi-Direction Hierarchical Representation