EHFP-GAN: Edge-Enhanced Hierarchical Feature Pyramid Network for Damaged QR Code Reconstruction

Abstract

In practical usage, QR codes often become difficult to recognize due to damage. Traditional restoration methods exhibit a limited effectiveness for severely damaged or densely encoded QR codes, are time-consuming, and have limitations in addressing extensive information loss. To tackle these challenges, we propose a two-stage restoration model named the EHFP-GAN, comprising an edge restoration module and a QR code reconstruction module. The edge restoration module guides subsequent restoration by repairing the edge images, resulting in finer edge details. The hierarchical feature pyramid within the QR code reconstruction module enhances the model’s global image perception. Using our custom dataset, we compare the EHFP-GAN against several mainstream image processing models. The results demonstrate the exceptional restoration performance of the EHFP-GAN model. Specifically, across various levels of contamination, the EHFP-GAN achieves significant improvements in the recognition rate and image quality metrics, surpassing the comparative models. For instance, under mild contamination, the EHFP-GAN achieves a recognition rate of 95.35%, while under a random contamination, it reaches 31.94%, both outperforming the comparative models. In conclusion, the EHFP-GAN model demonstrates remarkable efficacy in the restoration of damaged QR codes.