{"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/sharpness-aware-low-dose-ct-denoising-using","title":"Sharpness-aware Low dose CT denoising using conditional generative adversarial network","arxiv_id":"1708.06453","date":"2017-08-22","proceeding":null,"authors":["Xin Yi","Paul Babyn"],"abstract":"Low Dose Computed Tomography (LDCT) has offered tremendous benefits in\nradiation restricted applications, but the quantum noise as resulted by the\ninsufficient number of photons could potentially harm the diagnostic\nperformance. Current image-based denoising methods tend to produce a blur\neffect on the final reconstructed results especially in high noise levels. In\nthis paper, a deep learning based approach was proposed to mitigate this\nproblem. An adversarially trained network and a sharpness detection network\nwere trained to guide the training process. Experiments on both simulated and\nreal dataset shows that the results of the proposed method have very small\nresolution loss and achieves better performance relative to the-state-of-art\nmethods both quantitatively and visually.","url_abs":"http://arxiv.org/abs/1708.06453v2","url_pdf":"http://arxiv.org/pdf/1708.06453v2.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":"sharpness-aware-low-dose-ct-denoising-using","repo_url":"https://github.com/xinario/SAGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"torch","reach":null},{"paper_slug":"sharpness-aware-low-dose-ct-denoising-using","repo_url":"https://github.com/BH94/cGANs-tensorflow-Python","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}