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.

翻译：高分辨率正弦图补全对于计算机断层扫描重建至关重要，因为缺失的投影会引入严重的伪影。尽管扩散模型为此任务提供了强大的生成先验，但其推理成本随分辨率提升而急剧增加。我们提出了HRSino，一种用于高分辨率正弦图补全的无训练高效扩散推理方法。通过显式考虑信号特征（如频谱稀疏性和局部复杂性）的空间异质性，HRSino在空间区域和分辨率之间自适应地分配推理计算量，而非采用统一的高分辨率扩散步骤。这使得全局一致性能够在粗粒度尺度上被捕捉，同时仅在必要时细化局部细节。实验结果表明，与最先进框架相比，HRSino将峰值内存使用降低了高达30.81%，推理时间缩短了高达17.58%，并在不同数据集和分辨率下保持了补全精度。