Rain Removal from images based on Computer Vision and conditional Generative Adversarial Networks

Abstract

In view of the quick development of modern computer technology, more pictures of natural scenes have been collected and analyzed for application in military fields, scientific research, and computer vision fields related to object detection. However, the sharpness of these pictures collected will be affected by rain and snow, which will reduce the visibility of things in the pictures. This will not only affect the viewing, but also affect scientific research fields such as autonomous driving and video surveillance. Therefore, the research on image rain removing is very practical. An improved algorithm model named pixel-GAN is proposed in this article, which based on conditional Generative Adversarial Networks (cGAN). This new method regards the problem of removing rain from specific images as the style conversion problem. What we improved is that a VGG-16 module is added to the original network to extract perceptual loss as a new part of the loss function. We show the excellent rain-removing ability of the improved algorithm and also illustrate the reason why we add the perceptual loss. Compared with other existing methods, there is no need to set a prior model, the training speed is faster and the proposed model finally generates higher quality pictures.