A motion image deblurring algorithm based on a deep residual generative adversarial network is proposed for the motion image blurring problem arising from motion, jitter and electronic interference during image capture. Firstly, this paper investigates the image blurring model and the blind deblurring process. Secondly, the generative adversarial network is introduced, and the structure of the residual block is improved. The improved residual block contains three convolutional layers, two ReLU activation functions, a Dropout layer, and a skip connection block, which improves the quality of the recovered image. Thirdly, the structure of PatchGAN is improved, and the receptive field of the lowest layer is more than twice of the original one with only a few additional paramters and network complexity. The tests are conducted using the GOPRO dataset and Lai dataset. The test results show that the deblurring algorithm based on deep residual generation adversarial network proposed in this paper can achieve high objective evaluation indexes and can recover clear images of high quality. On the GOPRO dataset, compared with other similar methods, the algorithm proposed in this paper has better recovery ability and can achieve higher peak signal-to-noise ratio (28.31 dB) and higher structural similarity (0.831 7). On the Lai dataset, the higher quality images can be recovered.