Attention-Guided Deraining Network Via Stage-Wise Learning

Abstract

Due to diverse rain shapes, directions, densities as well as different distances to cameras, rain streaks in the air are interweaved and overlapped. However, most existing deraining methods are inherently oblivious this phenomenon and tend to learn a single rain streak layer to simulate this complex distribution, consequently failing to restore high-quality rain-free images. To solve this problem, along with the stage-wise learning, we propose a novel attention-guided deraining network (ADN) for rain streak removal. Specially, we decompose the rain streaks into multiple rain streak layers, and individually model them along the stages of the network to match the increasing abstracts. Moreover, the attention mechanism is utilized to guide the fusion of these rain streak layers by handling the overlaps between them. Extensive experiments on several benchmark datasets and real-world scenarios show substantial improvements both on quantitative indicators and visual effects over the current top-performing methods.