The DeblurGAN method uses Conditional Generative Adversarial Networks (cGANs) to solve the end-to-end image deblurring problem, but there are problems of insufficient image edge detail recovery and low robustness. Aiming at this problem, a blind restoration method of motion blurred images based on DeblurGAN is proposed. In the generative network, a multi-scale convolution kernel neural network is used to extract features, and cascaded atrous convolution is used to expand the receptive field of neurons; an adaptive normalization method is used to replace the instance normalization method used in the original generator. Second, the gradient image L1 loss is introduced, combined with adversarial loss and perceptual loss, as a regular constraint for image deblurring, making the edge features of the generated image clearer. The experimental results show that the peak signal-to-noise ratio of the image restored by the proposed method is 5.4% higher than that of the DeblurGAN algorithm, and the structural similarity index is 1% higher; the subjective clearing effect is better, and the grid effect is eliminated.