{"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/wavelet-domain-residual-network-wavresnet-for","title":"Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction","arxiv_id":"1703.01383","date":"2017-03-04","proceeding":null,"authors":["Eunhee Kang","Junhong Min","Jong Chul Ye"],"abstract":"Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT\nare computationally complex because of the repeated use of the forward and\nbackward projection. Inspired by this success of deep learning in computer\nvision applications, we recently proposed a deep convolutional neural network\n(CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT\nGrand Challenge. However, some of the texture are not fully recovered, which\nwas unfamiliar to some radiologists. To cope with this problem, here we propose\na direct residual learning approach on directional wavelet domain to solve this\nproblem and to improve the performance against previous work. In particular,\nthe new network estimates the noise of each input wavelet transform, and then\nthe de-noised wavelet coefficients are obtained by subtracting the noise from\nthe input wavelet transform bands. The experimental results confirm that the\nproposed network has significantly improved performance, preserving the detail\ntexture of the original images.","url_abs":"http://arxiv.org/abs/1703.01383v1","url_pdf":"http://arxiv.org/pdf/1703.01383v1.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":"wavelet-domain-residual-network-wavresnet-for","repo_url":"https://github.com/eunh/low_dose_CT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"wavelet-domain-residual-network-wavresnet-for","repo_url":"https://github.com/jongcye/deeplearningLDCT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"ct-reconstruction","task_name":"CT Reconstruction"},{"task_slug":"low-dose-x-ray-ct-reconstruction","task_name":"Low-Dose X-Ray 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}