论文标题
物理模型引导深度图像
Physical Model Guided Deep Image Deraining
论文作者
论文摘要
单图像是一项紧迫的任务，因为降低的多雨图像使许多计算机视觉系统无法正常工作，例如视频监视和自动驾驶。 因此，DERANES变得重要，需要有效的DERANE算法。 在本文中，我们提出了一个基于物理模型指导学习的新型网络，该网络由三个子网络组成：Rain Streaks网络，无雨网络和指南学习网络。 通过雨条条纹网络估计的雨条和无雨图像的串联分别是无雨网络估算的，是对指导学习网络的输入，以指导进一步的学习，并且两个估计图像的直接总和受到了基于雨水图像的物理模型的输入雨图像。 此外，我们进一步开发了多尺度残差块（MSRB），以更好地利用多尺度信息，并被证明可以提高降低性能。 定量和定性实验结果表明，所提出的方法的表现优于最先进的方法。 源代码将在\ url {https://supercong94.wixsite.com/supercong94}上获得。
Single image deraining is an urgent task because the degraded rainy image makes many computer vision systems fail to work, such as video surveillance and autonomous driving. So, deraining becomes important and an effective deraining algorithm is needed. In this paper, we propose a novel network based on physical model guided learning for single image deraining, which consists of three sub-networks: rain streaks network, rain-free network, and guide-learning network. The concatenation of rain streaks and rain-free image that are estimated by rain streaks network, rain-free network, respectively, is input to the guide-learning network to guide further learning and the direct sum of the two estimated images is constrained with the input rainy image based on the physical model of rainy image. Moreover, we further develop the Multi-Scale Residual Block (MSRB) to better utilize multi-scale information and it is proved to boost the deraining performance. Quantitative and qualitative experimental results demonstrate that the proposed method outperforms the state-of-the-art deraining methods. The source code will be available at \url{https://supercong94.wixsite.com/supercong94}.
