Quick response barcode deblurring via L0 regularisation based sparse optimisation

• Published in: IET image processing 2019-06, Vol.13 (8), p.1254-1258
• Main Authors: Zhang, Yan, Pu, Haitao, Lian, Jian
• Format: Article
• Language: English
• Subjects: bar codes; black blocks; computer vision; current machine vision systems; identification; image deblurring methods; image motion analysis; image restoration; image restoration techniques; information acquisition technology; L0 regularisation‐based approach; motion blurred two‐dimensional barcode images; optimisation; quick response barcode deblurring; Research Article; sparse optimisation; static barcodes; synthetically blurry barcode images; two‐dimensional barcode techniques; verification; white blocks
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/iet-ipr.2018.5738

In recent years, the two-dimensional barcode techniques have been widely applied in a large number of fields due to the requirement for the development of information acquisition technology. Also, the identification and verification of the static barcodes could be implemented flawlessly. However, the recognition of the motion blurred two-dimensional barcode images still remains a challenge to most of the current machine vision systems. To address the above-mentioned problem, the authors propose a $L_0$L0 regularisation-based approach to reverse the blurry barcode images while taking the number of black and white blocks in the two-dimensional barcode image as the prior. To evaluate the performance of the proposed approach, they conducted comparison experiments between state-of-the-art image deblurring methods and ours. Experimental results on synthetically blurry barcode images show that the proposed method outperforms the state-of-the-art image restoration techniques.