论文标题
通过尺度空间不变注意神经网络的单图像
Single Image Deraining via Scale-space Invariant Attention Neural Network
论文作者
论文摘要
降临雨伪影的降解的图像增强在户外视觉计算系统中起着至关重要的作用。在本文中，我们解决了规模概念，该概念涉及相对于相机的雨牛排外观的视觉变化。具体而言，我们按比例空间理论重新审视多尺度表示，并提议表示卷积特征域中的多尺度相关性，这比像素域中更紧凑，更健壮。此外，为了提高网络的建模能力，我们不会平等地处理提取的多尺度功能，而是设计一种新型的规模空间不变的注意机制，以帮助网络关注部分功能。通过这种方式，我们总结了特征地图最激活的存在为显着特征。关于合成和真实的雨场的广泛实验结果表明，我们计划的表现优于最先进的。
Image enhancement from degradation of rainy artifacts plays a critical role in outdoor visual computing systems. In this paper, we tackle the notion of scale that deals with visual changes in appearance of rain steaks with respect to the camera. Specifically, we revisit multi-scale representation by scale-space theory, and propose to represent the multi-scale correlation in convolutional feature domain, which is more compact and robust than that in pixel domain. Moreover, to improve the modeling ability of the network, we do not treat the extracted multi-scale features equally, but design a novel scale-space invariant attention mechanism to help the network focus on parts of the features. In this way, we summarize the most activated presence of feature maps as the salient features. Extensive experiments results on synthetic and real rainy scenes demonstrate the superior performance of our scheme over the state-of-the-arts.
