Blind image deblurring aims to recover the sharp image from a blurry image. The problem is seriously ill-conditioned and many existing algorithms based on kernel estimation require heuristic parameter adjustments and high computational cost, and cannot perform well on non-uniform motion blurs. To address this issue, image deblurring is viewed as an image translation problem in this paper. The authors solve it based on a conditional generative adversarial network (GAN), where the sharp image is restored by an end-to-end trainable neural network. Different from the generative network in basic conditional GAN, the proposed generator is based on dense blocks and residual network (DenseResNet), aiming to mitigate the problems of overfitting and vanishing gradient, and strengthen the blur feature propagation. To generate clear structure, the basic conditional GAN formulation is further revised by introducing joint VGG features and $L_1$-based gradient loss. Extensive experimental results demonstrate the superior performance of the proposed method.

盲图像去模糊旨在从模糊图像中恢复清晰图像。该问题病情严重，并且许多基于核估计的现有算法都需要启发式参数调整和高计算成本，并且在非均匀运动模糊上不能很好地执行。为了解决这个问题，在本文中将图像去模糊视为图像转换问题。作者基于条件生成对抗网络（GAN）解决了此问题，在该网络中，端到端可训练的神经网络可还原清晰图像。与基本条件GAN中的生成网络不同，该生成器基于密集块和残差网络（DenseResNet），旨在缓解梯度过度拟合和消失的问题，并增强模糊特征的传播。为了产生清晰的结构， ${\mathrm{大号}}_{\mathrm{1个}}$基于梯度的损失。大量的实验结果证明了该方法的优越性能。