无需训练，高效完成高分辨率投影数据补全
Training-Free Inference for High-Resolution Sinogram Completion
- 根据信号局部复杂度动态分配推理资源
- 内存占用降低30.81%，推理时间减少17.58%
- 适用于多数据集与多种分辨率的医学重建
高分辨率投影数据补全对计算机断层成像重建至关重要，缺失投影会引入严重伪影。尽管扩散模型具备强大的生成先验，但其推理开销随分辨率急剧上升。我们提出HRSino，一种无需训练且高效的高分辨率投影补全扩散推理方法。通过显式建模信号特征的空间异质性，如谱稀疏性和局部复杂度，HRSino在空间区域和分辨率间自适应分配推理资源，而非统一执行高分辨率扩散步骤。这使得粗尺度下捕捉全局一致性，仅在必要处细化局部细节。实验表明，相比当前最优框架，HRSino将峰值内存使用降低高达30.81%，推理时间减少最多17.58%，并在多个数据集和分辨率下保持完成精度。
原文摘要 · Abstract (English)
High-resolution sinogram completion is critical for computed tomography reconstruction, as missing projections can introduce severe artifacts. While diffusion models provide strong generative priors for this task, their inference cost grows prohibitively with resolution. We propose HRSino, a training-free and efficient diffusion inference approach for high-resolution sinogram completion. By explicitly accounting for spatial heterogeneity in signal characteristics, such as spectral sparsity and local complexity, HRSino allocates inference effort adaptively across spatial regions and resolutions, rather than applying uniform high-resolution diffusion steps. This enables global consistency to be captured at coarse scales while refining local details only where necessary. Experimental results show that HRSino reduces peak memory usage by up to 30.81% and inference time by up to 17.58% compared to the state-of-the-art framework, and maintains completion accuracy across datasets and resolutions.
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