为研究出一种快速且有效的图像去模糊方法，基于 DeblurGAN 提出一种利用 条件生成对抗网络实现的端到端图像去运动模糊方法。该方法将 DeblurGAN 的标准卷 积层改成瓶颈结构，并对瓶颈结构中的卷积进行低秩分解，且添加两个残差对称跳跃连 接，以加速网络收敛。为解决 DeblurGAN 复原图像不够清晰这个问题，向网络损失函 数添加互信息损失和梯度图像 L1 损失，通过最大化输入图像和其隐含特征间的互信息， 使所提取的隐含特征能很好地表征输入信息，从而利用隐含特征还原出清晰图像，而 L1 损失有利于使复原图像的边缘更明显。同时，通过实验对该方法的有效性进行了验 证，并与其他已有的同类算法进行了比较。结果表明: 相比 DeblurGAN，文中方法峰值 信噪比更高，两者的结构相似性指标相当，且文中模型参数量压缩至 DeblurGAN 的 3. 25% ，去模糊速度提高 3 倍，模型性能优于已有的其他同类算法。

An end-to-end image motion deblurring method based on DeblurGAN was proposed with conditional generative adversarial network． In this method，the standard convolution layers in DeblurGAN were changed into bottleneck structures，and low-rank decomposition was further performed on the convolution layers in the bottleneck structures． Then two residual symmetric skip connections were added to accelerate the convergence of the network． In order to solve the problem that the restored images in the DeblurGAN are not clear，mutual information loss and the gradient image L1 loss were added to the network loss function． By maximizing the mutual information between the input image and its hidden feature，the extracted hidden feature can well represent the input information， thereby obtaining a clear restored image from the hidden feature，and the L1 loss helps to make the restored image with more significant edge． At the same time，the effectiveness of the proposed method was verified by experiments and compared with other existing similar algorithms． The results show that compared with DeblurGAN，the peak signal-to-noise ratio of the proposed method is higher; the structural similarity measure of the two methods is equiv- alent; the parameter quantity of our model is compressed to 3. 25% of DeblurGAN; the deblurring processing speed is increased by 3 times，and our model outperforms other existing similar algorithms．