Enhancing the Scanability of Damaged QR Codes Through Image Restoration Using GANs Combined with the Spectral Normalization Technique

QR Codes are widely used in the digital era for storing and sharing information in various applications. However, they are often susceptible to physical damage such as scratches, tears, or fading, which can result in scanning failures and limit their usability. To overcome this issue, this research introduces a Generative Adversarial Network (GAN) model integrated with Spectral Normalization to restore damaged QR Code images. The model was trained and evaluated using a dataset of QR Codes with simulated damage ranging from 1% to 60%. Experimental results demonstrate that the proposed approach effectively reconstructs missing parts of QR Codes while preserving structural details and module sharpness. The model achieved an average PSNR of 28.5 dB, SSIM of 0.91, and a scanning success rate of 88%, outperforming U-Net (68%) and a baseline GAN (75%). Although the processing time is slightly longer, the model offers superior accuracy and robustness, particularly for severely damaged QR Codes (40% to 60% damage). These findings confirm that GANs enhanced with Spectral Normalization offer a promising solution for QR Code restoration, with potential uses in digital marketing, payment systems, and inventory management.