{"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/confidence-driven-tgv-fusion","title":"Confidence driven TGV fusion","arxiv_id":"1603.09302","date":"2016-03-30","proceeding":null,"authors":["Valsamis Ntouskos","Fiora Pirri"],"abstract":"We introduce a novel model for spatially varying variational data fusion,\ndriven by point-wise confidence values. The proposed model allows for the joint\nestimation of the data and the confidence values based on the spatial coherence\nof the data. We discuss the main properties of the introduced model as well as\nsuitable algorithms for estimating the solution of the corresponding biconvex\nminimization problem and their convergence. The performance of the proposed\nmodel is evaluated considering the problem of depth image fusion by using both\nsynthetic and real data from publicly available datasets.","url_abs":"http://arxiv.org/abs/1603.09302v2","url_pdf":"http://arxiv.org/pdf/1603.09302v2.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":"confidence-driven-tgv-fusion","repo_url":"https://github.com/alcor-vision/confidence-fusion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}