{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/intratomo-self-supervised-learning-based","title":"IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and Prediction","arxiv_id":null,"date":"2021-01-01","proceeding":"ICCV 2021 10","authors":["Guangming Zang","Ramzi Idoughi","Rui Li","Peter Wonka","Wolfgang Heidrich"],"abstract":"    We propose IntraTomo, a powerful framework that combines the benefits of learning-based and model-based approaches for solving highly ill-posed inverse problems in the Computed Tomography (CT) context. IntraTomo is composed of two core modules: a novel sinogram prediction module, and a geometry refinement module, which are applied iteratively. In the first module, the unknown density field is represented as a continuous and differentiable function, parameterized by a deep neural network. This network is learned, in a self-supervised fashion, from the incomplete or/and degraded input sinogram. After getting estimated through the sinogram prediction module, the density field is consistently refined in the second module using local and non-local geometrical priors. With these two core modules, we show that IntraTomo significantly outperforms existing approaches on several ill-posed inverse problems, such as limited angle tomography with a range of 45 degrees, sparse view tomographic reconstruction with as few as eight views, or super-resolution tomography with eight times increased resolution. The experiments on simulated and real data show that our approach can achieve results of unprecedented quality.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2021/html/Zang_IntraTomo_Self-Supervised_Learning-Based_Tomography_via_Sinogram_Synthesis_and_Prediction_ICCV_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2021/papers/Zang_IntraTomo_Self-Supervised_Learning-Based_Tomography_via_Sinogram_Synthesis_and_Prediction_ICCV_2021_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"intratomo-self-supervised-learning-based","repo_url":"https://github.com/vccimaging/intratomo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"low-dose-x-ray-ct-reconstruction","task_name":"Low-Dose X-Ray Ct Reconstruction"},{"task_slug":"novel-view-synthesis","task_name":"Novel View Synthesis"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/low-dose-x-ray-ct-reconstruction-on-x3d","task":"Low-Dose X-Ray Ct Reconstruction","dataset":"X3D","model":"InTomo","rank_in_archive_order":8,"of":9,"metrics":{"PSNR":"30.29","SSIM":"0.9189"},"uses_additional_data":false},{"leaderboard":"/sota/novel-view-synthesis-on-x3d","task":"Novel View Synthesis","dataset":"X3D","model":"InTomo","rank_in_archive_order":6,"of":6,"metrics":{"PSNR":"31.96","SSIM":"0.9768"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}