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Quick response barcode deblurring via L0 regularisation based sparse optimisation

机译:通过基于 L 0 正则化的稀疏优化实现快速响应的条形码去模糊

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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.
机译:近年来,由于对信息获取技术的发展的要求,二维条形码技术已经在许多领域中得到广泛应用。而且,可以完美地实现对静态条形码的识别和验证。然而,对于运动模糊的二维条形码图像的识别仍然对大多数当前的机器视觉系统构成挑战。为了解决上述问题,作者提出了一种基于$ L_0 $ L0正则化的方法来反转模糊的条形码图像,同时将二维条形码图像中黑白块的数量作为先验。为了评估所提出方法的性能,他们进行了最先进的图像去模糊方法与我们的图像去模糊方法之间的比较实验。综合模糊条形码图像的实验结果表明,所提出的方法优于最新的图像恢复技术。

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