In modern urban traffic systems, intersection monitoring systems are used to monitor traffic flows and track vehicles by recognizing license plates. However, intersection monitors often produce motion-blurred images because of the rapid movement of cars. If a deep learning network is used for image deblurring, the blurring of the image can be eliminated first, and then the complete vehicle information can be obtained to improve the recognition rate. To restore a dynamic blurred image to a sharp image, this paper proposes a multi-scale modified U-Net image deblurring network using dilated convolution and employs a variable scaling iterative strategy to make the scheme more adaptable to actual blurred images. Multi-scale architecture uses scale changes to learn the characteristics of different scales of images, and the use of dilated convolution can improve the advantages of the receptive field and obtain more information from features without increasing the computational cost. Experimental results are obtained using a synthetic motion-blurred image dataset and a real blurred image dataset for comparison with existing deblurring methods. The experimental results demonstrate that the image deblurring method proposed in this paper has a favorable effect on actual motion-blurred images.

中文翻译：

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使用扩张卷积通过多尺度修改 U-Net 进行图像去模糊

在现代城市交通系统中，路口监控系统用于监控交通流量并通过识别车牌来跟踪车辆。然而，由于汽车的快速移动，十字路口监视器经常会产生运动模糊的图像。如果采用深度学习网络进行图像去模糊，可以先消除图像的模糊，然后才能得到完整的车辆信息，提高识别率。为了将动态模糊图像恢复为清晰图像，本文提出了一种使用扩张卷积的多尺度改进U-Net图像去模糊网络，并采用可变缩放迭代策略使该方案更适应实际模糊图像。多尺度架构利用尺度变化来学习图像不同尺度的特征，而使用扩张卷积可以提高感受野的优势，在不增加计算成本的情况下从特征中获取更多信息。使用合成运动模糊图像数据集和真实模糊图像数据集获得实验结果，以与现有的去模糊方法进行比较。实验结果表明，本文提出的图像去模糊方法对实际运动模糊图像具有良好的效果。