{"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/fusing-multiple-multiband-images","title":"Fusing Multiple Multiband Images","arxiv_id":"1712.04575","date":"2017-12-13","proceeding":null,"authors":["Reza Arablouei"],"abstract":"We consider the problem of fusing an arbitrary number of multiband, i.e.,\npanchromatic, multispectral, or hyperspectral, images belonging to the same\nscene. We use the well-known forward observation and linear mixture models with\nGaussian perturbations to formulate the maximum-likelihood estimator of the\nendmember abundance matrix of the fused image. We calculate the Fisher\ninformation matrix for this estimator and examine the conditions for the\nuniqueness of the estimator. We use a vector total-variation penalty term\ntogether with nonnegativity and sum-to-one constraints on the endmember\nabundances to regularize the derived maximum-likelihood estimation problem. The\nregularization facilitates exploiting the prior knowledge that natural images\nare mostly composed of piecewise smooth regions with limited abrupt changes,\ni.e., edges, as well as coping with potential ill-posedness of the fusion\nproblem. We solve the resultant convex optimization problem using the\nalternating direction method of multipliers. We utilize the circular\nconvolution theorem in conjunction with the fast Fourier transform to alleviate\nthe computational complexity of the proposed algorithm. Experiments with\nmultiband images constructed from real hyperspectral datasets reveal the\nsuperior performance of the proposed algorithm in comparison with the\nstate-of-the-art algorithms, which need to be used in tandem to fuse more than\ntwo multiband images.","url_abs":"http://arxiv.org/abs/1712.04575v2","url_pdf":"http://arxiv.org/pdf/1712.04575v2.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":"fusing-multiple-multiband-images","repo_url":"https://github.com/Reza219/Multiple-multiband-image-fusion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"infrared-and-visible-image-fusion","task_name":"Infrared And Visible Image Fusion"}],"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}