  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.
