Abstract
Barcodes are widely used in supermarkets and tracking systems. One noticeable difficulty in barcode recognition is caused by blurring artifacts. For example, a focal blur may occur when the data is captured at a far distance, and motion blur is often inevitable under a low light condition. To decipher the barcode, many barcode reading systems may require more information than the image alone, e.g., the digits written below the barcode and/or a blurring kernel that describes how the data is blurred from a clean image. In this paper, we propose a method to recover a clean barcode from the blurred data without any additional information (e.g. information on the blurring kernel and so on). In particular, we propose a two-step procedure for blind deconvolution of barcode images. In the first step, we estimate the blurring kernel by exploiting a barcode structure with an exclusive search of the first two digits. Using the estimated kernel, we rely on a non-blind deconvolution technique to restore the entire barcode. We conduct numerical experiments based on both synthetic and real data, showing the efficiency and accuracy of the proposed method.
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Notes
- 1.
The default setting in the Matlab’s conv command is zero boundary condition.
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Acknowledgements
This research was initiated by a summer research program at the Interdisciplinary Computational Applied Mathematics Program (ICAMP) hosted by the University of California, Irvine. ICAMP was funded by the NSF grant DMS-0928427. We would like to thank Dr. Jack **n and late Dr. Ernie Esser for advising the summer research. Other students who involved in the early development of this work are **ling Zhang from Brown University’s Computer Science program and Max Hung who graduated from UCI with a B.S. in Computer Science. We would also like to thank the Research Collaboration Workshop for Women in Data Science and Mathematics held at ICERM from July 29 to August 2, 2019, to finish off this paper.
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Kim, B., Lou, Y. (2021). Two-Step Blind Deconvolution of UPC-A Barcode Images. In: Demir, I., Lou, Y., Wang, X., Welker, K. (eds) Advances in Data Science. Association for Women in Mathematics Series, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-030-79891-8_3
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