{"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/scale-invariant-structure-saliency-selection","title":"Scale-Invariant Structure Saliency Selection for Fast Image Fusion","arxiv_id":"1810.12553","date":"2018-10-30","proceeding":null,"authors":["Yixiong Liang","Yuan Mao","Jiazhi Xia","Yao Xiang","Jianfeng Liu"],"abstract":"In this paper, we present a fast yet effective method for pixel-level\nscale-invariant image fusion in spatial domain based on the scale-space theory.\nSpecifically, we propose a scale-invariant structure saliency selection scheme\nbased on the difference-of-Gaussian (DoG) pyramid of images to build the\nweights or activity map. Due to the scale-invariant structure saliency\nselection, our method can keep both details of small size objects and the\nintegrity information of large size objects in images. In addition, our method\nis very efficient since there are no complex operation involved and easy to be\nimplemented and therefore can be used for fast high resolution images fusion.\nExperimental results demonstrate the proposed method yields competitive or even\nbetter results comparing to state-of-the-art image fusion methods both in terms\nof visual quality and objective evaluation metrics. Furthermore, the proposed\nmethod is very fast and can be used to fuse the high resolution images in\nreal-time. Code is available at https://github.com/yiqingmy/Fusion.","url_abs":"http://arxiv.org/abs/1810.12553v1","url_pdf":"http://arxiv.org/pdf/1810.12553v1.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":"scale-invariant-structure-saliency-selection","repo_url":"https://github.com/yiqingmy/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}