{"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/deep-convolutional-framelet-denosing-for-low","title":"Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network","arxiv_id":"1707.09938","date":"2017-07-31","proceeding":null,"authors":["Eunhee Kang","Jaejun Yoo","Jong Chul Ye"],"abstract":"Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT\nare computationally expensive. To address this problem, we recently proposed a\ndeep convolutional neural network (CNN) for low-dose X-ray CT and won the\nsecond place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the\ntexture were not fully recovered. To address this problem, here we propose a\nnovel framelet-based denoising algorithm using wavelet residual network which\nsynergistically combines the expressive power of deep learning and the\nperformance guarantee from the framelet-based denoising algorithms. The new\nalgorithms were inspired by the recent interpretation of the deep convolutional\nneural network (CNN) as a cascaded convolution framelet signal representation.\nExtensive experimental results confirm that the proposed networks have\nsignificantly improved performance and preserves the detail texture of the\noriginal images.","url_abs":"http://arxiv.org/abs/1707.09938v3","url_pdf":"http://arxiv.org/pdf/1707.09938v3.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":"deep-convolutional-framelet-denosing-for-low","repo_url":"https://github.com/eunh/low_dose_CT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}