Classification Assistance Based Image Inpainting for BR code
Yuting Yang, Jinfeng Li, Naifeng Liang, Man Li, Xiangyun Cai, Ziyao Zheng · 2024
Recently, many image inpainting methods have achieved promising performance in recovering damaged natural images in various scenes. Different from natural images, blur-readable (BR) 2D barcode images have distinct structural characteristics, such as finder patterns (represent the version information) and data bits. Version information is crucial for decoding BR code. The problem of missing data bits can be solved by image inpainting methods and error correction codes. However, when the finder patterns are damaged, they may not be correctly repaired through the inpainting technology, resulting in the failure of barcode recognition. The limitation of existing image inpainting algorithms is that the version information of BR code cannot be obtained while generating repaired result. In this paper, we innovatively introduce the classification task into the inpainting network and propose a classification assistance based inpainting model for BR code (CAII). Experiment results show that our method can efficiently enhance the readability of damaged BR code images.