Abstract: Traditional methods of motion blur blind removal are required to predict the fuzzy kernel of the blurred images and then restore the clear images. However, the fuzzy kernel in the real environment is complex, causing such method to fail to reduce the difference between the actual image and the restored one. Moreover, the application of the popular generative adversarial model directly to image blurring blind removal will cause serious pattern collapse. Therefore, based on the characteristics of deblurring tasks, we proposed an end-to-end generative adversarial network model—two-frame generative adversarial network. The scheme does not need to predict the fuzzy kernel, and it can directly realize the blind removal of the motion blur of images. Based on the original CycleGan, the two-frame generative adversarial network improved its network structure and loss function to improve the accuracy of blind removal of moving images and greatly improve the stability of the network in the case of limited samples. The minimum mean square error was used to optimize the network training. Finally, a clear image was obtained by the adversarial training between generative network and discriminant network. Experimental results on the ILSVRC2015 VID dataset show that the method has a higher quality of restoration. And the restored results appear to be better in subsequent target detection tasks.

Key words: generative adversarial networks, blind motion blur, cycle consistency, conditional models