Motion Deblur of QR Code Based on Generative Adversative Network
Benzhi Wang, Jingben Xu, Junke Zhang, Guan‐Cheng Li, Xin Wang · 2019
Aiming at the problem that the QR codes cannot be recognized due to factors such as motion blur caused by the hand tremor and environmental noise in the QR codes of the packaged product, this paper proposes an algorithm based on the Generative Adversative Network to remove QR codes motion blur. Compared with the traditional deblurring algorithm, this method eliminates the process of blur kernel estimation, directly restores the motion blurred image. Gradient L1 regularization is added on the basis of the traditional GAN, which enhances the edge features of the restored image and adds the perceptual loss term, so that the image generated by the generator is closer to the global structure of the target clear image. The results show that the generator can remove the blur and generate a QR code with high recognition rate.