Generate adversarial network based on binary priors and conditions research on image deblurring algorithm of QR code

QR codes are black and white two-dimensional bar code graphics that record data information, with fast readability and powerful storage capabilities. With the rapid development of mobile communication and the popularity of intelligent devices, the speed of information exchange has been greatly accelerated, QR codes are widely used in various areas of life. This paper proposes a defuzzification algorithm based on binary priori properties and conditions of black and white pixels to generate adversarial networks. The potential feature representation of the QR code image is extracted by the selfsupervised pretraining of the encoder, which is used for the training of the conditional generation adverssion network. The trained generator network can realize the pixel-level binary segmentation restoration of the degraded QR code image with different fuzzy degrees. The experimental results show that the proposed network framework can recover the QR code images with different degrees of degradation end to end, pixel level, and improve the recognition rate.