@inproceedings{32e06c9bd34a4291a3b6896417c41ffb,
title = "Research on Varitional Pan-Sharpening Model with Adaptive Regex Coefficients",
abstract = "Pansharpening enhances the resolution of multispectral images by fusing them with higher-resolution panchromatic images, improving spatial resolution while retaining spectral information. This technique is valuable for visual interpretation and advanced processing. Recent advancements focus on enhancing spatial resolution through variational optimization, which transforms image fusion into an extremum problem. This paper introduces non-negative adaptive mixing coefficients to the pansharpening variational model by Fang et al., aiming to better align gradient curves of panchromatic and multispectral images, enhancing spatial information extraction while maintaining spectral quality. The proposed model{\textquoteright}s efficacy is verified through numerical experiments on Quickbird and GaoFen images, demonstrating superior spatial quality compared to traditional methods.",
keywords = "Adaptive coefficients, Energy functionals, Pansharpening",
author = "Yao Li and Tianyou Ma and Zhichang Guo",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 8th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2024 ; Conference date: 01-11-2024 Through 07-11-2024",
year = "2024",
doi = "10.1007/978-3-031-77411-9\_29",
language = "英语",
isbn = "9783031774102",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "321--330",
editor = "Sergey Kovalev and Andrey Sukhanov and Igor Kotenko and Yin Li and Yao Li",
booktitle = "Proceedings of the 8th International Scientific Conference {\textquotedblleft}Intelligent Information Technologies for Industry{\textquotedblright} (IITI{\textquoteright}24)",
address = "德国",
}