Fast restoration for out-of-focus blurred images of QR code with edge prior information via image sensing.

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.

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Service composition technology promises a new solution to an age-old problem: how to implement complex, large scale, cross-enterprise business application. However, we must ensure the compatibility between component services in composite service. Current research works in this field only concern semantics compatibility of service interface, yet ignoring the compatibility of service behavior. Therefore, this paper proposes a model of service behavior based on Petri-nets with weight. Based on the novel model, a method is proposed to verify and compute service behavior compatibility.