Research On Style Transfer Of QR Code Based On Convolutional Neural Network

With the popularization of mobile smart devices such as mobile phones,the continuous advancement of mobile Internet has spawned the rapid development of mobile Internet instant messaging and mobile payment.Quick Response(QR)code has become the information carrier of the mobile Internet world.Due to its fast scanning response and strong anti-injury capability,QR codes have been widely used in mobile payment,product tracking,project identification,time tracking,document management and general marketing.However,the defects of the standard QR code are also very obvious.Because the QR code is composed of black and white square modules,it lacks visual beauty,especially the color and shape are monotonous on advertisements such as advertisements with artistic designs.QR code reduces the artistic atmosphere of the entire work.Therefore,it is of great practical significance and certain application value to carry out research on the transfer of the artistic style of QR codes.This paper focuses on the research on the transfer task of QR codes based on convolutional neural networks.Through the network structure,network connection methods and the correction scheme of the QR codes of artistic styles,the transfer of the artistic style of QR codes is realized.The main contributions of this article are as follows:1.A QR code style transfer network(QRSTN)Aiming at the problem of single shape and monotonous color of QR code,the network is specially designed for the style transfer of QR code,starting from the structure and parameter settings of the style transfer network,and exploring the content and style loss weight according to experimental analysis Proportional distribution,as much as possible to retain the content information of the content image when transferring styles.Because the style transfer network will destroy the QR code standard detection and positioning graphics,it will change the shape and color,which will affect the scanning and positioning of the artistic QR code.This paper proposes a correction method for the art style QR code standard detection and positioning graphics,which greatly improves the detectability of the art style QR code.Finally,experiments show that the QRSTN method proposed in this paper has a very good effect on the transfer of QR code style.2.An improved QR code style transfer network(QRSTN_Improve)In order to maintain the stylized effect of QR codes while reducing the hardware requirements of the network and improving the efficiency of the style transfer network,this paper proposes an improved QR code style transfer network(QRSTN_Improve),which introduces dense connections In this way,without reducing the number of network layers,the style transfer effect of the network is not reduced,thereby improving the operating efficiency of the network to a certain extent.In this paper,based on the QR code standard detection and positioning graphics correction,this paper proposes an improved solution-artistic QR code weighted fusion correction program.The correction solution is not limited to standard detection and positioning graphics,but to the correction of the QR code of the entire artistic style QR code,so it can repair the destruction of some specific styles to the QR code information module,and enhance the artistic style QR code The recognizability also guarantees the integrity of the information module.Then this article proposes a dynamic QR code solution of artistic style,which increases the artistic aesthetic of QR code and the dynamic image has a strong visual appeal for human eyes and is more attractive to people.Attention,on the basis of this,the dynamic art style QR code can also have more expression forms,which broadens the application scene of the art style QR code.Finally,we prove through experiments that the QRSTN_Improve method has a good effect.The experimental results are judged from subjective visual observation and objective quantitative indicators,and the effectiveness of the QRSTN_Improve scheme is comprehensively verified.