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: this https URL

本文提出了一种基于 CNN 的雨水去除算法，即通过提取图像的 RCP 实现结构信息的保护和恢复，同时利用 Wavelet-based Multi-Level Module 作为网络的骨干，通过 Interactive Fusion Module 实现 RCP 信息的充分利用，以此生成更加清晰精细的去雨图像。

具有残差通道先验指导的结构保持去雨

Structure-Preserving Deraining with Residue Channel Prior Guidance

A deraining network can be interpreted as a conditional generator that aims
at removing rain streaks from image. Most existing image deraining methods
ignore model errors caused by uncertainty that reduces embedding quality.
Unlike existing image deraining methods that embed low-quality features into
the model directly, we replace low-quality features by latent high-quality
features. The spirit of closed-loop feedback in the automatic control field is
borrowed to obtain latent high-quality features. A new method for error
detection and feature compensation is proposed to address model errors.
Extensive experiments on benchmark datasets as well as specific real datasets
demonstrate that the proposed method outperforms recent state-of-the-art
methods. Code is available at: \\ this https URL

提出了一种新的降雨去除方法，通过替换低质量特征的潜在高质量特征和引入闭环反馈精神解决模型误差和特征嵌入问题，实验结果表明该方法在基准数据集和真实数据集上优于现有的最新方法。