  Low-quality face image restoration is a popular research direction in today's
computer vision field. It can be used as a pre-work for tasks such as face
detection and face recognition. At present, there is a lot of work to solve the
problem of low-quality faces under various environmental conditions. This paper
mainly focuses on the restoration of motion-blurred faces. In increasingly
abundant mobile scenes, the fast recovery of motion-blurred faces can bring
highly effective speed improvements in tasks such as face matching. In order to
achieve this goal, a deblurring method for motion-blurred facial image signals
based on generative adversarial networks(GANs) is proposed. It uses an
end-to-end method to train a sharp image generator, i.e., a processor for
motion-blurred facial images. This paper introduce the processing progress of
motion-blurred images, the development and changes of GANs and some basic
concepts. After that, it give the details of network structure and training
optimization design of the image processor. Then we conducted a motion blur
image generation experiment on some general facial data set, and used the pairs
of blurred and sharp face image data to perform the training and testing
experiments of the processor GAN, and gave some visual displays. Finally, MTCNN
is used to detect the faces of the image generated by the deblurring processor,
and compare it with the result of the blurred image. From the results, the
processing effect of the deblurring processor on the motion-blurred picture has
a significant improvement both in terms of intuition and evaluation indicators
of face detection.
