Deblurring Processor for Motion-Blurred Faces Based on Generative Adversarial Networks

TL;DR

This paper presents a GAN-based end-to-end deblurring method for motion-blurred facial images, significantly improving face detection accuracy in blurred images, with potential applications in mobile scene face recognition.

Contribution

It introduces a novel GAN-based deblurring network specifically designed for motion-blurred faces, with detailed network structure and training optimization, enhancing face detection performance.

Findings

Significant visual and quantitative improvements in deblurring quality.

Enhanced face detection accuracy on motion-blurred images.

Effective application of GANs for facial image restoration.

Abstract

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.