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QR code resolution improvement based on Super-Resolution Generative Adversarial Network
- 1 Northwest University
* Author to whom correspondence should be addressed.
Abstract
QR codes have become an integral part of our daily routines, simplifying tasks ranging from accessing websites to making payments. However, the quality of QR codes, especially their resolution, can significantly impact their functionality. Low-resolution QR codes may lead to misinterpretation during scanning and even decoding failures. To address this issue, researchers have explored various techniques to enhance the resolution of QR codes. Traditional image processing methods, such as interpolation and filtering, have been used in the past for resolution enhancement. However, these methods often result in overly blurry images with poor perceptual quality. Conversely, solutions based on Convolutional Neural Networks (CNNs) can introduce clarity but may compromise the sharpness of image edges. This paper presents an effective approach to improve QR code resolution using a Super-Resolution Generative Adversarial Network (SRGAN). The results are impressive, with SRGAN achieving a Peak Signal-to-Noise Ratio (PSNR) of 30.06, significantly outperforming the 17.48 achieved by the SRCNN method. Additionally, in terms of Structural Similarity Index (SSIM), SRGAN reaches 0.936, surpassing SRCNN's 0.473. These metrics demonstrate that SRGAN is highly effective in enhancing the resolution of QR codes, ensuring better scan accuracy and overall functionality in practical applications.
Keywords
QR Codes, Super-Resolution, Generative Adversarial Network, Convolutional Neural Networks
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Cite this article
Du,M. (2024). QR code resolution improvement based on Super-Resolution Generative Adversarial Network. Applied and Computational Engineering,51,6-13.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning
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