{"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/highres-net-recursive-fusion-for-multi-frame","title":"HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery","arxiv_id":"2002.06460","date":"2020-02-15","proceeding":null,"authors":["Michel Deudon","Alfredo Kalaitzis","Israel Goytom","Md Rifat Arefin","Zhichao Lin","Kris Sankaran","Vincent Michalski","Samira E. Kahou","Julien Cornebise","Yoshua Bengio"],"abstract":"Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views. This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery. To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion: (i) co-registration, (ii) fusion, (iii) up-sampling, and (iv) registration-at-the-loss. Co-registration of low-resolution views is learned implicitly through a reference-frame channel, with no explicit registration mechanism. We learn a global fusion operator that is applied recursively on an arbitrary number of low-resolution pairs. We introduce a registered loss, by learning to align the SR output to a ground-truth through ShiftNet. We show that by learning deep representations of multiple views, we can super-resolve low-resolution signals and enhance Earth Observation data at scale. Our approach recently topped the European Space Agency's MFSR competition on real-world satellite imagery.","url_abs":"https://arxiv.org/abs/2002.06460v1","url_pdf":"https://arxiv.org/pdf/2002.06460v1.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":"highres-net-recursive-fusion-for-multi-frame","repo_url":"https://github.com/ElementAI/HighRes-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"highres-net-recursive-fusion-for-multi-frame","repo_url":"https://github.com/aimiokab/misr-s2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"de-aliasing","task_name":"De-aliasing"},{"task_slug":"earth-observation","task_name":"Earth Observation"},{"task_slug":"image-registration","task_name":"Image Registration"},{"task_slug":"multi-frame-super-resolution","task_name":"Multi-Frame Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-frame-super-resolution-on-proba-v","task":"Multi-Frame Super-Resolution","dataset":"PROBA-V","model":"HighRes-net","rank_in_archive_order":6,"of":8,"metrics":{"Normalized cPSNR":"0.947388637793901"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2002.06460","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}