{"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/blind-image-fusion-for-hyperspectral-imaging","title":"Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation","arxiv_id":"1710.05705","date":"2017-10-04","proceeding":null,"authors":["Leon Bungert","David A. Coomes","Matthias J. Ehrhardt","Jennifer Rasch","Rafael Reisenhofer","Carola-Bibiane Schönlieb"],"abstract":"Hyperspectral imaging is a cutting-edge type of remote sensing used for\nmapping vegetation properties, rock minerals and other materials. A major\ndrawback of hyperspectral imaging devices is their intrinsic low spatial\nresolution. In this paper, we propose a method for increasing the spatial\nresolution of a hyperspectral image by fusing it with an image of higher\nspatial resolution that was obtained with a different imaging modality. This is\naccomplished by solving a variational problem in which the regularization\nfunctional is the directional total variation. To accommodate for possible\nmis-registrations between the two images, we consider a non-convex blind\nsuper-resolution problem where both a fused image and the corresponding\nconvolution kernel are estimated. Using this approach, our model can realign\nthe given images if needed. Our experimental results indicate that the\nnon-convexity is negligible in practice and that reliable solutions can be\ncomputed using a variety of different optimization algorithms. Numerical\nresults on real remote sensing data from plant sciences and urban monitoring\nshow the potential of the proposed method and suggests that it is robust with\nrespect to the regularization parameters, mis-registration and the shape of the\nkernel.","url_abs":"http://arxiv.org/abs/1710.05705v4","url_pdf":"http://arxiv.org/pdf/1710.05705v4.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":"blind-image-fusion-for-hyperspectral-imaging","repo_url":"https://github.com/leon-bungert/blind_remote_sensing","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"blind-image-fusion-for-hyperspectral-imaging","repo_url":"https://github.com/mehrhardt/blind_remote_sensing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"blind-super-resolution","task_name":"Blind Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"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}