{"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-learning-for-multiple-image-super","title":"Deep Learning for Multiple-Image Super-Resolution","arxiv_id":"1903.00440","date":"2019-03-01","proceeding":null,"authors":["Michal Kawulok","Pawel Benecki","Szymon Piechaczek","Krzysztof Hrynczenko","Daniel Kostrzewa","Jakub Nalepa"],"abstract":"Super-resolution reconstruction (SRR) is a process aimed at enhancing spatial\nresolution of images, either from a single observation, based on the learned\nrelation between low and high resolution, or from multiple images presenting\nthe same scene. SRR is particularly valuable, if it is infeasible to acquire\nimages at desired resolution, but many images of the same scene are available\nat lower resolution---this is inherent to a variety of remote sensing\nscenarios. Recently, we have witnessed substantial improvement in single-image\nSRR attributed to the use of deep neural networks for learning the relation\nbetween low and high resolution. Importantly, deep learning has not been\nexploited for multiple-image SRR, which benefits from information fusion and in\ngeneral allows for achieving higher reconstruction accuracy. In this letter, we\nintroduce a new method which combines the advantages of multiple-image fusion\nwith learning the low-to-high resolution mapping using deep networks. The\nreported experimental results indicate that our algorithm outperforms the\nstate-of-the-art SRR methods, including these that operate from a single image,\nas well as those that perform multiple-image fusion.","url_abs":"http://arxiv.org/abs/1903.00440v1","url_pdf":"http://arxiv.org/pdf/1903.00440v1.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-learning-for-multiple-image-super","repo_url":"https://github.com/ajinkya933/Image_repo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1903.00440","atlas_url":"https://app.syntology.ai/?focus=1903.00440","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}