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，因为缺失投影会引入严重伪影。尽管扩散模型为此任务提供了强大的生成先验，但其推理成本会随分辨率提升而急剧增加。我们提出HRSino，一种无需训练且高效的扩散推理方法，专门用于高分辨率正弦图补全。通过显式考虑信号特征中的空间异质性（如谱稀疏性与局部复杂度），HRSino能够自适应地在不同空间区域和分辨率间分配推理计算量，而非统一执行高分辨率扩散步骤。这使得全局一致性可在粗尺度上捕获，而局部细节仅在必要时进行精细化处理。实验结果表明，与现有最优框架相比，HRSino将峰值内存使用降低达30.81%，推理时间降低达17.58%，并在不同数据集和分辨率下保持补全精度。