论文标题
通过雨staks的单一图像意识到深度卷积神经网络
Single Image Deraining via Rain-Steaks Aware Deep Convolutional Neural Network
论文作者
论文摘要
从单个下雨的图像中取出雨牛排是一项挑战，因为雨牛排在下雨的图像中在空间上有所不同。本文通过结合常规图像处理技术和深度学习技术来研究此问题。提出了改进的加权引导图像过滤器（IWGIF），以从多雨的图像中提取高频信息。高频信息主要包括雨牛排和噪音，它可以指导雨牛排意识到深度卷积神经网络（RSADCNN），以更多地注意雨牛排。 RSADNN的效率和解释能力得到了提高。实验表明，就定性和定量测量而言，所提出的算法在合成和现实世界图像上的最先进方法都显着胜过最先进的方法。它对于在降雨条件下自主导航很有用。
It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. This problem is studied in this paper by combining conventional image processing techniques and deep learning based techniques. An improved weighted guided image filter (iWGIF) is proposed to extract high frequency information from a rainy image. The high frequency information mainly includes rain steaks and noise, and it can guide the rain steaks aware deep convolutional neural network (RSADCNN) to pay more attention to rain steaks. The efficiency and explain-ability of RSADNN are improved. Experiments show that the proposed algorithm significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. It is useful for autonomous navigation in raining conditions.
