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.
翻译:低质量面部图像的恢复是当今计算机视觉领域流行的研究方向。 它可以用作像脸部探测和面部识别这样的任务前工作。 目前,在各种环境条件下,要解决低质量面部问题需要做大量工作。 本文主要侧重于恢复运动模糊面部。 在日益丰富的移动场景中,运动模糊面部的快速恢复可以带来对面等任务高度有效的速度改进。 为了实现这一目标,可以提出一种基于基因化对立式移动网络(GANs)的运动模糊面部图像信号的模糊方法。 它使用端对端方法来训练一个清晰的图像生成器,即运动模糊面部面部图像图像的处理器。 本文介绍了移动模糊图像图像的处理过程、 GAN 的开发和改变以及一些基本概念。 之后,我们从面部对面部对面部图像的清晰度处理器进行了移动模糊的图像生成实验,然后用GAN 图像的清晰度测试和直观图像的图像检测结果, 将GAN 的图像的图像测试和图像的图像检测结果, 用于GR 的图像的图像的对比和图像的测试, 测试和最终的测试结果, 测试, 和图像的图像的图像的测试结果的对比和图像的测试结果, 和图像的对比的测试和图像的测试结果, 和图像的对比的对比的对比和图像的测试, 和图像的对比的对比的测试, 和图像的测试, 和图像的显示结果的对比的图像的测试, 和图像的图像的测试结果的对比的测试和图像的测试和图像的对比的图像的图像的对比的对比的对比的对比的测试, 。