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We present a novel direction-aware feature-level frequency decompositionnetwork for single image deraining. Compared with existing solutions, theproposed network has three compelling characteristics. First, unlike previousalgorithms, we propose to perform frequency decomposition at feature-levelinstead of image-level, allowing both low-frequency maps containing structuresand high-frequency maps containing details to be continuously refined duringthe training procedure. Second, we further establish communication channelsbetween low-frequency maps and high-frequency maps to interactively capturestructures from high-frequency maps and add them back to low-frequency mapsand, simultaneously, extract details from low-frequency maps and send them backto high-frequency maps, thereby removing rain streaks while preserving moredelicate features in the input image. Third, different from existing algorithmsusing convolutional filters consistent in all directions, we propose adirection-aware filter to capture the direction of rain streaks in order tomore effectively and thoroughly purge the input images of rain streaks. Weextensively evaluate the proposed approach in three representative datasets andexperimental results corroborate our approach consistently outperformsstate-of-the-art deraining algorithms.

Direction-aware Feature-level Frequency Decomposition for Single Image Deraining