Two-stage residual conditional diffusion network for super- resolution reconstruction of remote sensing images
Image Processing|更新时间:2025-12-12
|
Two-stage residual conditional diffusion network for super- resolution reconstruction of remote sensing images
“In the field of remote sensing image super-resolution reconstruction, the TRCDSR network effectively improves the reconstruction quality and computational efficiency through a two-stage residual conditional diffusion mechanism.”
Chinese Journal of Liquid Crystals and DisplaysVol. 40, Issue 11, Pages: 1647-1660(2025)
作者机构:
1.湘潭大学 自动化与电子信息学院, 湖南 湘潭 411100
2.湖南国家应用数学中心, 湖南湘潭411105
3.北京空间机电研究所, 北京 100094
作者简介:
基金信息:
National Key Research and Development Program(2020YFA0713503);Hunan Provincial Department of Education Hunan Provincial Teaching Reform Research Project for Regular Higher Education Institutions(HNJG-20230279);Hunan Provincial Department of Education Scientific Research Project(23C0059)
BU Lijing, CHEN Xiangxue, ZHANG Zhengpeng, et al. Two-stage residual conditional diffusion network for super- resolution reconstruction of remote sensing images[J]. Chinese Journal of Liquid Crystals and Displays, 2025, 40(11): 1647-1660.
DOI:
BU Lijing, CHEN Xiangxue, ZHANG Zhengpeng, et al. Two-stage residual conditional diffusion network for super- resolution reconstruction of remote sensing images[J]. Chinese Journal of Liquid Crystals and Displays, 2025, 40(11): 1647-1660. DOI: 10.37188/CJLCD.2025-0158. CSTR: 32172.14.CJLCD.2025-0158.
Two-stage residual conditional diffusion network for super- resolution reconstruction of remote sensing images