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

Jianhua Zheng, Ruolin Zhao, Zhongju Lin (), Shuangyin Liu, Rong Zhu, Zihao Zhang, Yusha Fu and Junde Lu
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Jianhua Zheng: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Ruolin Zhao: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Zhongju Lin: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Shuangyin Liu: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Rong Zhu: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Zihao Zhang: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Yusha Fu: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Junde Lu: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China

Mathematics, 2023, vol. 11, issue 20, 1-19

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.

Keywords: image inpainting; damaged QR code reconstruction; gan (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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