The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • GIDP 2026
  • ICONS_BA 2025

Computer Vision Conference (CVC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • RSS Feed

DOI: 10.14569/IJACSA.2025.0161066
PDF

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

Author 1: Puwadol Sirikongtham
Author 2: Apichaya Nimkoompai

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 10, 2025.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: 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.

Keywords: QR Code restoration; Generative Adversarial Networks; spectral normalization; image inpainting; deep learning; damage reconstruction

Puwadol Sirikongtham and Apichaya Nimkoompai. “Enhancing the Scanability of Damaged QR Codes Through Image Restoration Using GANs Combined with the Spectral Normalization Technique”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.10 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161066

@article{Sirikongtham2025,
title = {Enhancing the Scanability of Damaged QR Codes Through Image Restoration Using GANs Combined with the Spectral Normalization Technique},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161066},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161066},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {10},
author = {Puwadol Sirikongtham and Apichaya Nimkoompai}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

Computer Vision Conference (CVC) 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

Artificial Intelligence Conference 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computer Vision Conference
  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org