Unrolling a rain-guided detail recovery network for singleimage deraining

Abstract

BackgroundOwing to the rapid development of deep networks, single-image deraining tasks have progressed significantly. Various architectures have been designed to recursively or directly remove rain, and most rain streaks can be removed using existing deraining methods. However, many of them cause detail loss, resulting in visual artifacts. MethodTo resolve this issue, we propose a novel unrolling rain-guided detail recovery network (URDRN) for single-image deraining based on the observation that the most degraded areas of a background image tend to be the most rain-corrupted regions. Furthermore, to address the problem that most existing deep-learningbased methods trivialize the observation model and simply learn end-to-end mapping, the proposed URDRN unrolls a single-image deraining task into two subproblems: rain extraction and detail recovery. ResultSpecifically, first, a context aggregation attention network is introduced to effectively extract rain streaks; thereafter, a rain attention map is generated as an indicator to guide the detail recovery process. For the detail recovery sub-network, with the guidance of the rain attention map, a simple encoder–decoder model is sufficient to recover the lost details. Experiments on several well-known benchmark datasets show that the proposed approach can achieve performance similar to those of other state-of-the-art methods.