基于多通道分离整合的多尺度单幅图像去雨算法

由于密度不同的雨对图像造成的遮挡不同，图像去雨一直都是一项极具挑战性的任务。目前，基于深度学习的图像去雨算法已经成为主流。然而，多数深度学习的架构都是通过堆叠卷积层来设计的，执行去雨任务后图像仍存在着大小不一的雨痕，这些方法并不能很好地关注训练中雨图的局部信息和上下文信息。为了解决上述问题，本文设计一种基于多通道分离整合的卷积神经网络用于图像去雨。第一步通过通道分离，再利用卷积层间的层级连接，构成多尺度模块，最终将不同通道的输出进行整合。该模块可以增大感受野，探索特征图之间的空间信息，更好地提取特征。第二步利用渐进网络来反复计算挖掘上下文信息，能够很好关联到全局特征。整体模型易于实施，可以端对端训练。在常用的数据集以及自建的自动驾驶雨天数据集上的大量实验表明，本文方法比现有方法取得了明显的改进。

An Efficient Entity Identification Method for Electric BiddingDocuments Based on Conditional Random Field

Due to the occlusion of the image caused by the different density of rain, it has always been a challenging task to remove the rain from the images. Nowadays, image de-raining algorithms based on deep learning have become the mainstream. However, most deep learning architectures are designed by stacking convolutional layers. For the rain removal task, the images have rain streaks in different sizes. To address this issue, A convolutional neural network based on multi-channel separation and integration for single image rain removal is designed. In the first step, the separable channels and the hierarchical connection between the convolutional layers form a multi-scale module. Finally, the outputs of different channels are integrated. The proposed module can expand the receptive field and explore the spatial information between feature maps, which extracts features better. In the second step, progressive networks are exploited to repeatedly calculate and excavate contextual information, which can be well related to global features. The proposed model is easy to be implemented and can be trained end-to-end. Extensive experiments on widely used datasets and self-built rainy images dataset for autonomous driving demonstrate that the proposed method has achieved significant improvements over the existing methods.