{"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-deep-image-restoration-via-1","title":"Self-supervised deep image restoration via adaptive stochastic gradient Langevin dynamics","arxiv_id":null,"date":"2022-06-19","proceeding":"IEEE / CVF Computer Vision and Pattern Recognition Conference 2022 6","authors":["Weixi Wang; Ji Li; Hui Ji"],"abstract":"While supervised deep learning has been a prominent tool for solving many image restoration problems, there is an increasing interest on studying self-supervised or un- supervised methods to address the challenges and costs of collecting truth images. Based on the neuralization of a Bayesian estimator of the problem, this paper presents a self-supervised deep learning approach to general image restoration problems. The key ingredient of the neuralized estimator is an adaptive stochastic gradient Langevin dy- namics algorithm for efficiently sampling the posterior distri- bution of network weights. The proposed method is applied on two image restoration problems: compressed sensing and phase retrieval. The experiments on these applications showed that the proposed method not only outperformed existing non-learning and unsupervised solutions in terms of image restoration quality, but also is more computationally efficient.","url_abs":"https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Self-Supervised_Deep_Image_Restoration_via_Adaptive_Stochastic_Gradient_Langevin_Dynamics_CVPR_2022_paper.html","url_pdf":"https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Self-Supervised_Deep_Image_Restoration_via_Adaptive_Stochastic_Gradient_Langevin_Dynamics_CVPR_2022_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-deep-image-restoration-via-1","repo_url":"https://github.com/wang-weixi/restricted_sampling","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"self-supervised-deep-image-restoration-via-1","repo_url":"https://github.com/Wang-weixi/Adaptive_sampling","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-restoration","task_name":"Image Restoration"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"compressed-sensing","task_name":"compressed sensing"}],"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}