点击次数：

论文类型：期刊论文

发表时间：2020-09-01

发表刊物：APPLIED INTELLIGENCE

收录刊物：SCIE

卷号：50

期号：9

页面范围：2932-2944

ISSN号：0924-669X

关键字：Single image de-raining; Convolutional Neural Network (CNN); Squeeze-and-excitation; Non-local mean; Dense network

摘要：Raindrop blur or rain streaks can severely degrade the visual quality of the images, which causes many practical vision systems to fail to work, such as autonomous driving and video surveillance. Hence, it is important to address the problem of single image de-raining. In this paper, we propose a novel deep network for single image de-raining. The proposed network consists of three stages, including encoder stage, Dense Non-Local Residual Block (DNLRB) stage, and decoder stage. As spatial contextual information has been analyzed to be meaningful for image de-raining (Huang et al. ??), we adopt squeeze-and-excitation enhancing on feature maps in each convolution layer for capturing spatial contextual information. In addition, to better leverage spatial contextual information for extracting rain components, the non-local mean operation has been embed in DNLRB. Both quantitative and qualitative experimental results demonstrate the proposed method performs favorably against the state-of-the-art de-raining methods. The source codes will be available at .