Scratched Coating QR Code Image Restoration based on the Pluralistic Image Completion Deep Learning Method

In recent years, with the rapid development of digital printing technology, an increasing number of counterfeit products have entered the market. The anti-counterfeiting technique for QR Codes has been attracting increasing attention nowadays. There exist many image inpainting methods that can be applied in the image restoration field. Some image completion methods may restore coating QR Code images to a point where the covered digital number underneath is revealed even though the original coating QR Code has not been scratched off. In this paper, we extend the pluralistic image completion (PIC) method to scratched coating QR Code image restoration. Based on the binary characteristic of QR Codes, we present a specific type of deep learning model for scratched coating QR Code image completion. Experimental results demonstrate that the extended PIC is an effective approach to the restoration of scratched coating QR Code images.