用四叉树自适应选择图像细节区，高效提升超分辨率质量
QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-Resolution
- 基于输入图像构建四叉树，仅在细节区域进行精细计算
- 在医学影像上实现高保真重建，计算量减少超50%
- 适合资源受限设备，尤其擅长处理大片均匀区域
基于深度学习的超分辨率方法通常对全图统一进行像素级计算，即使在内容均一区域也进行冗余的高分辨率优化。本文提出四叉树扩散模型（QDM），一种基于四叉树结构的自适应扩散框架，可识别需增强的细节区域（叶节点），并在均一区域大幅减少计算量。该模型采用掩码引导的双流架构，在保证图像保真度的同时显著降低冗余计算。实验表明，QDM在多种图像类型上的高分辨率超分任务中表现优异，尤其在医学影像（如CT扫描）中效果突出，其计算成本远低于现有方法，性能与主流模型相当或更优，具备在资源受限环境中的部署潜力。代码已开源。
原文摘要 · Abstract (English)
Deep learning-based super-resolution (SR) methods often perform pixel-wise computations uniformly across entire images, even in homogeneous regions where high-resolution refinement is redundant. We propose the Quadtree Diffusion Model (QDM), a region-adaptive diffusion framework that leverages a quadtree structure to selectively enhance detail-rich regions while reducing computations in homogeneous areas. By guiding the diffusion with a quadtree derived from the low-quality input, QDM identifies key regions-represented by leaf nodes-where fine detail is essential and applies minimal refinement elsewhere. This mask-guided, two-stream architecture adaptively balances quality and efficiency, producing high-fidelity outputs with low computational redundancy. Experiments demonstrate QDM's effectiveness in high-resolution SR tasks across diverse image types, particularly in medical imaging (e.g., CT scans), where large homogeneous regions are prevalent. Furthermore, QDM outperforms or is comparable to state-of-the-art SR methods on standard benchmarks while significantly reducing computational costs, highlighting its efficiency and suitability for resource-limited environments. Our code is available at https://github.com/linYDTHU/QDM.
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