Single Image Deraining via detail-guided Efficient Channel Attention Network

Abstract Single image deraining is an important problem in many computer vision tasks since rain streaks can severely hamper and degrade the visibility of images. Exisiting methods either focus on extracting rain streaks and ignore the background recovery, or the network structure is extremely complex and the number of parameters is quite large. Although some methods mention background restoration work, they generally ignore effective contextual information and result in unsatisfactory results. In this paper, we propose a novel network single image Deraining via detail-guided Efficient Channel Attention Network (DECAN) to remove rain streaks from rainy images. Specifically, we introduce two sub-networks with a comprehensive loss function that synergize to remove rain streaks and recover the background of the derained image. For completing rain streaks removal, we construct a rain streaks removal network with detail-guided efficient-channel-attention module to identify effective low-level features. For background recovery, we present a specialized background repair network consisting of well-designed blocks, named background details recovery network, to repair the background with effective contextual information for eliminating image degradations. Experiments on four synthetic datasets and some real-world rainy image sets show visual and numerical improvements of proposed method over the state-of-the-arts considerably.