【摘要】 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.更多还原

【Abstract】 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 multidirectional 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.更多还原