  Single image deraining is important for many high-level computer vision tasks
since the rain streaks can severely degrade the visibility of images, thereby
affecting the recognition and analysis of the image. Recently, many CNN-based
methods have been proposed for rain removal. Although these methods can remove
part of the rain streaks, it is difficult for them to adapt to real-world
scenarios and restore high-quality rain-free images with clear and accurate
structures. To solve this problem, we propose a Structure-Preserving Deraining
Network (SPDNet) with RCP guidance. SPDNet directly generates high-quality
rain-free images with clear and accurate structures under the guidance of RCP
but does not rely on any rain-generating assumptions. Specifically, we found
that the RCP of images contains more accurate structural information than rainy
images. Therefore, we introduced it to our deraining network to protect
structure information of the rain-free image. Meanwhile, a Wavelet-based
Multi-Level Module (WMLM) is proposed as the backbone for learning the
background information of rainy images and an Interactive Fusion Module (IFM)
is designed to make full use of RCP information. In addition, an iterative
guidance strategy is proposed to gradually improve the accuracy of RCP,
refining the result in a progressive path. Extensive experimental results on
both synthetic and real-world datasets demonstrate that the proposed model
achieves new state-of-the-art results. Code: https://github.com/Joyies/SPDNet
