{"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/self-supervised-cryo-electron-tomography","title":"Self-Supervised Cryo-Electron Tomography Volumetric Image Restoration From Single Noisy Volume With Sparsity Constraint","arxiv_id":null,"date":"2021-01-01","proceeding":"ICCV 2021 10","authors":["Zhidong Yang","Fa Zhang","Renmin Han"],"abstract":"    Cryo-Electron Tomography (cryo-ET) is a powerful tool for 3D cellular visualization. Due to instrumental limitations, cryo-ET images and their volumetric reconstruction suffer from extremely low signal-to-noise ratio. In this paper, we propose a novel end-to-end self-supervised learning model, the Sparsity Constrained Network (SC-Net), to restore volumetric image from single noisy data in cryo-ET. The proposed method only requires a single noisy data as training input and no ground-truth is needed in the whole training procedure. A new target function is proposed to preserve both local smoothness and detailed structure. Additionally, a novel procedure for the simulation of electron tomographic photographing is designed to help the evaluation of methods. Experiments are done on three simulated data and four real-world data. The results show that our method could produce a strong enhancement for a single very noisy cryo-ET volumetric data, which is much better than the state-of-the-art Noise2Void, and with a competitive performance comparing with Noise2Noise. Code is available at https://github.com/icthrm/SC-Net.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2021/html/Yang_Self-Supervised_Cryo-Electron_Tomography_Volumetric_Image_Restoration_From_Single_Noisy_Volume_ICCV_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2021/papers/Yang_Self-Supervised_Cryo-Electron_Tomography_Volumetric_Image_Restoration_From_Single_Noisy_Volume_ICCV_2021_paper.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":"self-supervised-cryo-electron-tomography","repo_url":"https://github.com/icthrm/sc-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"electron-tomography","task_name":"Electron Tomography"},{"task_slug":"image-restoration","task_name":"Image Restoration"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}