DiffQRCoder：基于扩散的美学QR码生成，具备扫描鲁棒性引导的迭代细化
DiffQRCoder: Diffusion-based Aesthetic QR Code Generation with Scanning Robustness Guided Iterative Refinement
AI总结：
本文提出无需训练的DiffQRCoder，通过SRPG扩散引导与SR-MPGD潜空间迭代优化，在保持QR码视觉美感的同时显著提升扫描鲁棒性。
摘要：
随着扩散模型在图像生成中的成功，这些技术也革新了美学快速响应（QR）码生成。尽管美化后的QR码在视觉吸引力方面取得了显著提升，但其可扫描性通常被牺牲，从而阻碍了它们在真实场景中的实际使用。为解决这一问题，我们提出了一种新颖的、无需训练的基于扩散的QR码生成器（DiffQRCoder），以有效生成既可扫描又视觉悦目的QR码。所提方法引入了扫描鲁棒感知引导（SRPG），这是一种面向扩散模型的新型扩散引导，用于确保生成的美学码在去噪过程中遵循真实QR码，同时保持其吸引力。此外，我们提出了另一种后处理技术——扫描鲁棒流形投影梯度下降（SR-MPGD），通过迭代潜空间优化进一步增强其扫描鲁棒性。大量实验结果表明，我们的方法不仅在扫描成功率（SSR）上优于其他对比方法，并具有更好或相当的CLIP美学评分（CLIP-aes.），而且将仅使用ControlNet方法的SSR从60%显著提升至99%。主观评价表明，我们的方法也为用户带来了良好的视觉吸引力。最后，即使在不同扫描角度和最严格的容错设置下，我们的方法仍能稳健地实现超过95%的SSR，展示了其面向真实应用的能力。我们的项目页面位于 https://jwliao1209.github.io/DiffQRCoder。
英文摘要：
With the success of Diffusion Models for image generation, the technologies also have revolutionized the aesthetic Quick Response (QR) code generation. Despite significant improvements in visual attractiveness for the beautified codes, their scannabilities are usually sacrificed and thus hinder their practical uses in real-world scenarios. To address this issue, we propose a novel training-free Diffusion-based QR Code generator (DiffQRCoder) to effectively craft both scannable and visually pleasing QR codes. The proposed approach introduces Scanning-Robust Perceptual Guidance (SRPG), a new diffusion guidance for Diffusion Models to guarantee the generated aesthetic codes to obey the ground-truth QR codes while maintaining their attractiveness during the denoising process. Additionally, we present another post-processing technique, Scanning Robust Manifold Projected Gradient Descent (SR-MPGD), to further enhance their scanning robustness through iterative latent space optimization. With extensive experiments, the results demonstrate that our approach not only outperforms other compared methods in Scanning Success Rate (SSR) with better or comparable CLIP aesthetic score (CLIP-aes.) but also significantly improves the SSR of the ControlNet-only approach from 60% to 99%. The subjective evaluation indicates that our approach achieves promising visual attractiveness to users as well. Finally, even with different scanning angles and the most rigorous error tolerance settings, our approach robustly achieves over 95% SSR, demonstrating its capability for real-world applications. Our project page is available at https://jwliao1209.github.io/DiffQRCoder.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
