针对模糊二维码的快速去模糊，提升解码成功率。
An Adaptive Edge-Guided Dual-Network Framework for Fast QR Code Motion Deblurring
- 用边缘引导注意力块显式利用二维码结构先验
- 严重模糊下解码率显著提升，轻量网络实现快速处理
- 自适应双网络根据模糊程度切换，适合移动端
与注重视觉质量的一般图像去模糊不同，二维码去模糊的核心目标是保证解码成功。二维码具有高度结构化的图案和锐利边缘，可作为修复的重要先验信息。然而现有深度学习方法很少显式利用此类先验。为此，本文提出边缘引导注意力块（EGAB），将显式边缘先验嵌入Transformer架构。基于EGAB，构建边缘引导Restormer（EG-Restormer），显著提升严重模糊二维码的解码率。对于轻微模糊输入，设计轻量高效网络（LENet）以实现快速去模糊。进一步将两网络融合为自适应双网络（ADNet），根据输入模糊程度动态选择合适网络，适用于资源受限的移动设备。大量实验表明，EG-Restormer与ADNet在保持竞争速度的同时达到顶尖性能。
原文摘要 · Abstract (English)
Unlike general image deblurring that prioritizes perceptual quality, QR code deblurring focuses on ensuring successful decoding. QR codes are characterized by highly structured patterns with sharp edges, a robust prior for restoration. Yet existing deep learning methods rarely exploit these priors explicitly. To address this gap, we propose the Edge-Guided Attention Block (EGAB), which embeds explicit edge priors into a Transformer architecture. Based on EGAB, we develop Edge-Guided Restormer (EG-Restormer), an effective network that significantly boosts the decoding rate of severely blurred QR codes. For mildly blurred inputs, we design the Lightweight and Efficient Network (LENet) for fast deblurring. We further integrate these two networks into an Adaptive Dual-network (ADNet), which dynamically selects the suitable network based on input blur severity, making it ideal for resource-constrained mobile devices. Extensive experiments show that our EG-Restormer and ADNet achieve state-of-the-art performance with a competitive speed. Project page: https://github.com/leejianping/ADNet
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