{"type":"ScholarlyArticle","title":"Training-Free Inference for High-Resolution Sinogram Completion","authors":[{"name":"Jiaze E","slug":"jiaze-e","given":"Jiaze","family":"E","affiliation":"Argonne National Laboratory"},{"name":"Srutarshi Banerjee","slug":"srutarshi-banerjee","given":"Srutarshi","family":"Banerjee","affiliation":"Argonne National Laboratory; University of Chicago"},{"name":"Tekin Bicer","slug":"tekin-bicer","given":"Tekin","family":"Bicer","affiliation":""},{"name":"Guannan Wang","slug":"guannan-wang","given":"Guannan","family":"Wang","affiliation":""},{"name":"Yanfu Zhang","slug":"yanfu-zhang","given":"Yanfu","family":"Zhang","affiliation":""},{"name":"Bin Ren","slug":"bin-ren","given":"Bin","family":"Ren","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2026/119","url":"https://ijcai.org/proceedings/2026/119","pdf":"https://ijcai.org/proceedings/2026/0119.pdf","markdown":"https://ijcai.org/proceedings/2026/119.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/119","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":119,"pages":{"first":1061,"last":1069},"keywords":["Computer Vision: Applications and Systems"],"videos":[],"references":[{"position":1,"raw":"[Armato III et al., 2011] Samuel G Armato III, Geof- frey McLennan, Luc Bidaut, Michael F McNitt-Gray, Charles R Meyer, Anthony P Reeves, Binsheng Zhao, Denise R Aberle, Claudia I Henschke, Eric A Hoffman, et al. 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