GladCoder: Stylized QR Code Generation with Grayscale-Aware Denoising Process

Traditional QR codes consist of a grid of black-and-white square modules, which lack aesthetic appeal and meaning for human perception. This has motivated recent research to beautify the visual appearance of QR codes. However, there exists a trade-off between the visual quality and scanning-robustness of the image, causing outputs of previous works are simple and of low quality to ensure scanning-robustness. In this paper, we introduce a novel approach GladCoder to generate stylized QR codes that are personalized, natural, and text-driven. Its pipeline includes a Depth-guided Aesthetic QR code Generator (DAG) to improve quality of image foreground, and a GrayscaLe-Aware Denoising (GLAD) process to enhance scanning-robustness. The overall pipeline is based on diffusion models, which allow users to create stylized QR images from a textual prompt to describe the image and a textual input to be encoded. Experiments demonstrate that our method can generate stylized QR code with appealing perception details, while maintaining robust scanning reliability under real world applications.

Authors

• Yuqiu Xie Tsinghua Shenzhen International Graduate School, Tsinghua University
• Bolin Jiang
• Jiawei Li University of Science and Technology Beijing
• Naiqi Li Tsinghua Shenzhen International Graduate School
• Bin Chen Harbin Institute of Technology, Shenzhen Research Center of Artificial Intelligence, Peng Cheng Laboratory
• Tao Dai Shenzhen University
• Yuang Peng
• Shu-Tao Xia Tsinghua Shenzhen International Graduate School Pengcheng Laboratory

Keywords

• Application domains: Images, movies and visual arts