{"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/scalable-double-regularization-for-3d-nano-ct","title":"Scalable Double Regularization for 3D Nano-CT Reconstruction","arxiv_id":"1909.02256","date":"2020-01-14","proceeding":null,"authors":[],"abstract":"Nano-CT (computerized tomography) has emerged as a non-destructive\nhigh-resolution cross-sectional imaging technique to effectively study the\nsub-$\\mu$m pore structure of shale, which is of fundamental importance to the\nevaluation and development of shale oil and gas. Nano-CT poses unique\nchallenges to the inverse problem of reconstructing the 3D structure due to the\nlower signal-to-noise ratio (than Micro-CT) at the nano-scale, increased\nsensitivity to the misaligned geometry caused by the movement of object\nmanipulator, limited sample size, and a larger volume of data at higher\nresolution. In this paper, we propose a scalable double regularization (SDR)\nmethod to utilize the entire dataset for simultaneous 3D structural\nreconstruction across slices through total variation regularization within\nslices and $L_1$ regularization between adjacent slices. SDR allows information\nborrowing both within and between slices, contrasting with the traditional\nmethods that usually build on slice by slice reconstruction. We develop a\nscalable and memory-efficient algorithm by exploiting the systematic sparsity\nand consistent geometry induced by such Nano-CT data. We illustrate the\nproposed method using synthetic data and two Nano-CT imaging datasets of\nJiulaodong (JLD) shale and Longmaxi (LMX) shale acquired in the Sichuan Basin.\nThese numerical experiments show that the proposed method substantially\noutperforms selected alternatives both visually and quantitatively.","url_abs":"http://arxiv.org/abs/1909.02256v3","url_pdf":"http://arxiv.org/pdf/1909.02256v3.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":"scalable-double-regularization-for-3d-nano-ct","repo_url":"https://github.com/xylimeng/SDR-CT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"ct-reconstruction","task_name":"CT Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}