De-blurring of faces in video based on GAN prior
Datao Xie, Lei Yu, Xingyu Geng · 2023
Until now, the video de-blurring is still a challenging task. In many video de-blurring tasks, high-quality face images in videos is particularly important. However, due to the mutual motion between the camera and the subject during video recording, motion blur often occurs, especially in some frames the blurring of face is particularly serious. But before or after this frame, it may be possible to obtain different local information of the blurred face. For this reason, this paper proposes a new video face de-blurring method based on GAN prior. First, we learn the GAN prior network that generates high-quality face images, and embed it into the whole network as a prior module. Then, residual dense blocks are used in the RNN cell to extract the spatial features of the face in the current frame. In addition, a fusion module is used to fuse the hierarchical features of past and future frames to de-blur the current frame and generate a low quality low fuzzy face. Finally, this face is sent to a trained prior generator to generate a high-quality face. After a series of experiments, the results show that the de-blurring effect of the method we proposed is better than the existing algorithm.