Multi-residual generative adversarial networks for QR code deblurring

Mingyue Wang; Kecheng Chen; Fanqiang Lin

doi:10.1117/12.2640025

23 May 2022 Multi-residual generative adversarial networks for QR code deblurring

Mingyue Wang, Kecheng Chen, Fanqiang Lin

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 122542H (2022) https://doi.org/10.1117/12.2640025
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

Motion blur and ambient noise are the main reasons that affect quick response (QR) code recognition. In this paper, we propose a novel deep learning approach to deblur the QR codes and realize the effective recognition of deblurring QR codes by using generative adversarial networks (GANs). We estimate the blur kernel and ambient noise of the blur QR code in the dataset using GANs, so as to realize the transformation from the blur QR code image to the sharp image. We also propose an expansion method of QR codes dataset, and achieve better generalization performance of the model. The experimental results show that our approach can effectively estimate the blur kernel and ambient noise that can realize the deblurring of QR code.

Citation Download Citation

Mingyue Wang, Kecheng Chen, and Fanqiang Lin "Multi-residual generative adversarial networks for QR code deblurring", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 122542H (23 May 2022); https://doi.org/10.1117/12.2640025

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KEYWORDS

Image filtering

Gallium nitride

Image restoration

Feedback loops

Convolution

Data modeling

Image processing

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