With the development of 5G technology, the short delay requirements of commercialization and large amounts of data change our lifestyle day-to-day. In this background, this paper proposes a fast blind deblurring algorithm for QR code images, which mainly achieves the effect of adaptive scale control by introducing an evaluation mechanism. Its main purpose is to solve the out-of-focus caused by lens shake, inaccurate focus, and optical noise by speeding up the latent image estimation in the process of multi-scale division iterative deblurring. The algorithm optimizes productivity under the guidance of collaborative computing, based on the characteristics of the QR codes, such as the features of gradient and strength. In the evaluation step, the Tenengrad method is used to evaluate the image quality, and the evaluation value is compared with the empirical value obtained from the experimental data. Combining with the error correction capability, the recognizable QR codes will be output. In addition, we introduced a scale control parameter to study the relationship between the recognition rate and restoration time. Theoretical analysis and experimental results show that the proposed algorithm has high recovery efficiency and well recovery effect, can be effectively applied in industrial applications.

随着5G技术的发展，商用化和海量数据的短时延需求改变了我们的日常生活。在此背景下，本文提出了一种二维码图像的快速盲去模糊算法，主要通过引入评价机制来达到自适应尺度控制的效果。其主要目的是通过加速多尺度分割迭代去模糊过程中的潜像估计，解决镜头抖动、对焦不准确、光学噪声等造成的离焦问题。该算法根据二维码的梯度、强度等特征，在协同计算的指导下优化生产力。在评估步骤中，使用 Tenengrad 方法评估图像质量，并将评价值与实验数据得到的经验值进行比较。结合纠错能力，输出可识别的二维码。此外，我们引入了一个尺度控制参数来研究识别率和恢复时间之间的关系。理论分析和实验结果表明，该算法具有较高的采收率和良好的采收效果，可有效应用于工业应用。