Single image deraining is a fundamental pre-processing step in many computer vision applications for improving the visual effect and system performance of the downstream high-level tasks in adverse weather conditions. This study proposes a novel multi-scale context aggregation network, to effectively solve the single image deraining problem. Specifically, we exploit a lightweight residual structure subnet as the baseline architecture to extract fine and detailed texture context at the original scale and further incorporate a multi-scale progressive aggregation module (MPAM) to learn the complementary high-level context for enhancing the modeling capability of the overall deraining network. The MPAM, designed as a plug-and-play module to be utilized in the arbitrary network, is composed of multi-scale convolution blocks to learn a wide variety of contexts in multiple receptive fields, and then carries out progressive context aggregation between adjacent scales with residual connections, which is expected to concurrently disentangle the multi-scale structures of scene contents and multiple rain layers in the rainy images, and models more representative contexts for reconstructing the clean image. To reduce the learnable parameters in the MPAM, we further explore a context hallucinate block for replacing the multi-scale convolution block, and propose a lightweight MPAM. Moreover, for being specially adaptive to deal with the input rainy images with a lot of unwanted components (rain layers), we delve into the artifact-attenuating pooling and activation functions via taking into consideration of the surrounding spatial context instead of pixel-wise operation and propose the spatial context-aware pooling (SCAP) and activation (SCAA) for incorporating with our deraining network to boost performance. Extensive experiments on the benchmark datasets demonstrate that our proposed method performs favorably against state-of-the-art deraining approaches.INDEX TERMS Deep residual block, multi-scale progressive aggregation, context hallucinate block, artifact-attenuating pooling and activation, image deraining

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Rain streak removal in a single image is a very challenging task due to its ill-posed nature in essence. Recently, the end-to-end learning techniques with deep convolutional neural networks (DCNN) have made great progress in this task. However, the conventional DCNN-based deraining methods have struggled to exploit deeper and more complex network architectures for pursuing better performance. This study proposes a novel MCGKT-Net for boosting deraining performance, which is a naturally multi-scale learning framework being capable of exploring multi-scale attributes of rain streaks and different semantic structures of the clear images. In order to obtain high representative features inside MCGKT-Net, we explore internal knowledge transfer module using ConvLSTM unit for conducting interaction learning between different layers and investigate external knowledge transfer module for leveraging the knowledge already learned in other task domains. Furthermore, to dynamically select useful features in learning procedure, we propose a multi-scale context gating module in the MCGKT-Net using squeeze-and-excitation block. Experiments on three benchmark datasets: Rain100H, Rain100L, and Rain800, manifest impressive performance compared with state-of-the-art methods.