{"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/pseudo-siamese-blind-spot-transformers-for","title":"Pseudo-Siamese Blind-Spot Transformers for Self-Supervised Real-World Denoising","arxiv_id":null,"date":"2025-06-05","proceeding":"The Annual Conference on Neural Information Processing Systems 2025 6","authors":["Yuhui Quan; Tianxiang Zheng; Hui Ji"],"abstract":"Real-world image denoising remains a challenge task. This paper studies self-supervised image denoising, requiring only noisy images captured in a single shot. We revamping the blind-spot technique by leveraging the transformer’s capability for long-range pixel interactions, which is crucial for effectively removing noise dependence in relating pixel–a requirement for achieving great performance for the blind-spot technique. The proposed method integrates these elements with two key innovations: a directional self-attention (DSA) module using a halfplane grid for self-attention, creating a sophisticated blind-spot structure, and a Siamese architecture with mutual learning to mitigate the performance impacts from the restricted attention grid in DSA. Experiments on benchmark datasets demonstrate that our method outperforms existing self-supervised and clean-imagefree methods. This combination of blind-spot and transformer techniques provides a natural synergy for tackling real-world image denoising challenges.","url_abs":"https://dl.acm.org/doi/10.5555/3737916.3738358","url_pdf":"https://proceedings.neurips.cc/paper_files/paper/2024/file/19305d2dbcc81c44d4a0120e7569856e-Paper-Conference.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":"pseudo-siamese-blind-spot-transformers-for","repo_url":"https://github.com/cszhengtx/SelfFormer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}