单张图像去雨的多流细节加强网络

对于低等级的计算机视觉任务来说，图像去雨一直是一个热点问题.由于图像中雨线的密度不均一，导致单张图片中去雨成为极富有挑战性的问题.针对目标图像重点关注的两个部分：图像的整体结构和图像的细节，本文提出一种新颖的多流特征融合的卷积神经网络算法，通过多样的网络框架呈现优越的性能.该网络算法采用三条分支网络提取复杂多向的雨线特征，并运用级联的方式特征融合，通过与原图像结合去除有雨图的雨线，再经过细节加强网络获得高质量的无雨图.在合成的数据集以及真实雨图集下的去雨性能表明，所提出的算法与现有的基于深度学习的去雨算法相比，能够在去除雨线的同时保留更多的细节，保证了图片的质量.

Single Image De-Raining Using Multi-Stream Detail Enhanced Network

For low-level computer vision tasks, image de-raining has always been a hot issue. However, due to the uneven density of rain lines in the image, it is a very challenging issue to remove rain from a single image. Attention to the target image often requires attention to two parts:the overall structure of the image and the details of the image. In this regard, a novel multi-stream feature fusion convolutional neural network algorithm is proposed. It presents superior performance through various network frameworks. The network algorithm uses three branch networks to extract complex multi-directional rain line features, concat features and combines with the original image to remove rain. The detail enhanced network can obtain high quality without rain. The de-raining performance under the synthesized dataset and the real rain dataset indicates that the proposed algorithm can preserve more details while removing the rain line than the existing deep learning-based rain removal algorithm, and it can keep the high quality of the picture.