{"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/trex-a-tomography-reconstruction-proximal","title":"TRex: A Tomography Reconstruction Proximal Framework for Robust Sparse View X-Ray Applications","arxiv_id":"1606.03601","date":"2016-06-11","proceeding":null,"authors":["Mohamed Aly","Guangming Zang","Wolfgang Heidrich","Peter Wonka"],"abstract":"We present TRex, a flexible and robust Tomographic Reconstruction framework\nusing proximal algorithms. We provide an overview and perform an experimental\ncomparison between the famous iterative reconstruction methods in terms of\nreconstruction quality in sparse view situations. We then derive the proximal\noperators for the four best methods. We show the flexibility of our framework\nby deriving solvers for two noise models: Gaussian and Poisson; and by plugging\nin three powerful regularizers. We compare our framework to state of the art\nmethods, and show superior quality on both synthetic and real datasets.","url_abs":"http://arxiv.org/abs/1606.03601v1","url_pdf":"http://arxiv.org/pdf/1606.03601v1.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":"trex-a-tomography-reconstruction-proximal","repo_url":"https://github.com/mohamedadaly/TRex","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"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}