Pyramid fully residual network for single image de-raining

Abstract

Rain removal from a single image is a challenging and significant task of image pre-processing. In this paper, we learn the multi-scale streaks from rainy images using feature pyramid, and to improve the effectiveness of the learning, we focus on the feature propagation re-usage and propagation in the extremely deep de-raining network. Specifically, we design a de-raining2 unit and propose a novel deep de-raining network, respectively, called Pyramid Fully Residual Unit and Network (PFR-Unit and PFR-Net). The PFR-Unit employs fully residual learning in each level of feature pyramid and the PFR-Net connects PFR-Units by a compact dense architecture. The fully residual learning encourages the feature re-usage in PFR-Unit by performing identity mapping for all available shortcuts. The compact dense connection strengthens the feature propagation between the PFR-Units and ensures the unicity of the learning space for the PFR-Units. Along with negative SSIM loss, the PFR-Net presents a good performance in single image de-raining. Comprehensive experimental results show that the PFR-Net outperforms the state-of-the-art single de-raining methods with a big margin on Rain100H, Rain100L and Rain1200 datasets.