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Style transfer for QR code

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Abstract

Due to fast scanning response and strong damage resistance, Quick Response (QR) code has been used widely in product tracking, item identification, time tracking, document management, and general marketing. The standard QR code consisting of black and white modules, has the following defects: the monotonous color pattern and the poor visual effect. To address these issues, we propose a style transfer method of QR code based on convolutional neural network. Next, we modify the convolutional neural network structure to be in line with the requirements of the style transfer of QR code, which can more preserve the content of the original image than the classic style transfer method. Furthermore, to increase the recognizability of the artistic style QR code, we modify the standard positioning point of the style transfer to generate the QR code. Experiments show that the proposed method has strong robustness.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No.61762012, No.61462026, and No.61762014, the Science and Technology Project of Guangxi Grant No.2018JJA170083, Innovation Project of Guangxi Graduate Education Grant (No.JGY2019023 and No.JGY2017018).

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Authors and Affiliations

  1. College of Electronic Engineering, Guangxi Normal University, Guilin, Guangxi, 541004, People’s Republic of China

    Hai-Sheng Li, Fan Xue & Hai-ying Xia

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  1. Hai-Sheng Li
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  2. Fan Xue
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  3. Hai-ying Xia
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Correspondence to Fan Xue.

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Li, HS., Xue, F. & Xia, Hy. Style transfer for QR code. Multimed Tools Appl 79, 33839–33852 (2020). https://doi.org/10.1007/s11042-019-08555-4

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