Two-Frame Convolutional Neural Network for Blind Motion Image Deblurring

To achieve blind motion image deblurring by an end-to-end full network,a method without estimating blur kernel was proposed.Firstly,this paper designed a two-frame convolutional neural network which consists of a generative model G and a discriminative model D.The model G is a full-convolutional networks,which can map from blurred image to the deburred image.The model D is an improved VGG for classification,which was used for determining whether the input image is the deburred image or the original clear one.Then,the minimum mean square error was adopted for optimizing network by adding image fidelity items to improve the deblurring results.Finally,the deburred image can be obtained by executing adversarial training on the model G and the model D.Experiments were executed on MS COCO dataset,the results demonstrate that the proposed method can improve the quality of the blurred image and reduce the time-consuming.