A facial motion blur removal method based on generative adversarial networks
When taking portraits,due to the vibration of photography equipment or the movement of people,the image may experience motion blur,which seriously affects the image quality.In scenarios such as image tracking or access control recognition,if there is motion blur on the screen,it may lead to the inability to recognize the target,resulting in the failure of tasks such as localization,recognition and tracking.Therefore,removing motion blur plays a crucial role in facial recognition applications.In this paper,we propose a method based on generation countermeasure network for motion blur of face images,which introduces multiple jump connections into the codec structure,introduces the features extracted in the convolution process into the deconvolution process,improves the reuse of feature information through global jump connections,reduces the learning complexity,and finally adjusts the weight of the loss function to obtain the end-to-end network from the blurred image to the repaired image.The experimental results show that this method has a good effect in eliminating facial motion blur and also improves the restoration of details such as facial contours.
facial imagesmotion blurgenerating adversarial networksloss function
NETL
NSTL
万方数据
