A Hybrid Approach Using MAE‑ViT Enhancement and Inpainting for the Detection and Reconstruction of Damaged QR Codes

Abstract

The use of Quick Response codes has become a standard in various digital applications, yet they remain highly susceptible to physical degradation. Standard decoding libraries generally perform well under ideal conditions but experience reduced accuracy when processing damaged codes. This study proposes a hybrid preprocessing approach that combines Masked Autoencoder–Vision Transformer (MAE–ViT) enhancement and mask-guided inpainting to improve the detection and reconstruction of damaged QR codes. Although light, medium, and heavy degradations are examined, the primary target regime is moderate damage near the decoder failure boundary. The enhancement stage refines global and local visual structure to improve contrast, edge sharpness, and module visibility, while the inpainting stage reconstructs missing or corrupted regions that still disrupt scanability. By applying this hybrid pipeline before standard decoding with ZXing, ZBar, and OpenCV, the method functions as a decoder-assistance module rather than replacing conventional decoders. Experimental results show that the proposed approach improves decoding success on damaged QR codes relative to direct baseline decoding, with the strongest gains under moderate degradation, while restoration quality and processing time are reported as supporting metrics. A supplementary real-world hold-out evaluation is also discussed to clarify generalization limits. The hybrid MAE–ViT framework therefore provides a practical solution for enhancing QR scanability under recoverable damage conditions.