Reconstructing unreadable QR codes: a deep learning based super resolution strategy

Quick-response (QR) codes have become an integral component of the digital transformation process, facilitating fast and secure information sharing across various sectors. However, factors such as low resolution, misalignment, panning and rotation, often caused by the limitations of scanning devices, can significantly impact their readability. These distortions prevent reliable extraction of embedded data, increase processing times and pose potential security risks. In this study, four super-resolution models Enhanced Deep Super Resolution (ESDR) network, Very Deep Super Resolution (VDSR) network, Efficient Sub-Pixel Convolutional Network (ESPCN) and Super Resolution Convolutional Neural Network (SRCNN) are used to mitigate resolution loss, rotation errors and misalignment issues. To simulate scanner-induced distortions, a dataset of 16,000 computer-generated QR codes with various filters was used. In addition, super-resolution models were applied to 4,593 QR codes that OpenCV's QRCodeDetector function could not decode in real-world scans. The results showed that EDSR, VDSR, ESPCN and SRCNN successfully read 4,261, 4,229, 4,255 and 4,042 of these QR codes, respectively. Furthermore, the EDSR, VDSR, ESPCN and SRCNN models trained by OpenCV's deep learning-based WeChat QR Code Detector function to read 2,899 QR codes that were initially unreadable and simulated on the computer were able to successfully read 2,891, 2,884, 2,433 and 2,560 of them, respectively. These findings show that super-resolution models can effectively improve the readability of degraded or low-resolution QR codes.