用于单张图像去雨滴的轻量级网络

图像去雨是图像低等级任务中的热点问题,去雨滴又是图像去雨中很重要的一种情况,附着在玻璃或相机镜头上的雨滴会显著降低场景的可见性.因此,去除雨滴将有助于许多计算机视觉应用,特别是户外监控系统和智能驾驶系统.本文提出了一种用于单张图像去雨滴的轻量级网络算法(PRSEDNet),该网络算法采用递归计算,运用卷积长短期记忆网络(Convolutional LSTM network)和特征提取模块来提取特征,通过与原图像结合来去除雨滴,最终获得高质量的无雨滴清晰图.实验结果表明,我们的PRSEDNet与现有的基于深度学习的去雨滴算法相比,在能达到高效的去雨滴性能的同时,有更少的参数量且计算效率高.

Lightweight Network for Single Image Raindrop Removal

Image de-raining is a hot issue in the low-level tasks of images, in which raindrop removal is critical. Raindrops adhering to glass or camera lenses will significantly reduce the visibility of the scenes. Therefore, removing raindrops will benefit various computer vision tasks, especially outdoor surveillance systems and intelligent driving systems. In this study, we propose a lightweight network (PRSEDNet) to remove raindrops from a single image. With recursive computation, this network uses a convolutional long short-term memory network and a feature extraction module to extract features. In combination with the original image, the final high-quality clear image without raindrops is obtained. The experimental results show that PRSEDNet, compared with the existing deep learning-based raindrop removal algorithms, possesses few parameters and high computational efficiency and can achieve efficient raindrop removal.