液晶与显示2025，Vol.40Issue(11)：1647-1660,14.DOI:10.37188/CJLCD.2025-0158
基于两阶段残差条件扩散网络的遥感图像超分辨重建
Two-stage residual conditional diffusion network for super-resolution reconstruction of remote sensing images
摘要
Abstract
When the traditional diffusion model is used for super-resolution reconstruction of remote sensing images,there are difficulties such as insufficient utilization of a priori conditions,lengthy sampling steps,and poor recovery of high frequency details.In this paper,we propose a two-stage residual conditional diffusion super-resolution network(TRCDSR).In the first stage generates preliminary super-resolution results with a pre-trained lightweight CNN model to provide a high-quality structural priori for the diffusion model.In the second stage,we introduce the residual conditional diffusion mechanism,which takes the residual signals as the input to let the noise prediction network to focus on the high-frequency detail reconstruction.By improving the DDIM inverse sampling formula,the residual correction process is decoupled into a deterministic prediction term and a random noise term,and the high-quality reconstruction is completed in 20～50 steps.The multi-scale prior condition enhancement module(PCEM)and the fusion of spatial and channel attention mechanism(FAN)are further introduced to enhance the model's adaptability to complex remote sensing scenes.Experiments on several remote sensing datasets,such as AID,SECOND,RSSCN,etc.,show that TRCDSR outperforms other diffusion models,GAN and Transformer-like methods in terms of reconstruction quality,computational efficiency and generalization ability.关键词
扩散模型/遥感超分辨率重建/残差网络/先验条件增强Key words
diffusion model/remote sensing super-resolution reconstruction/residual network/enhancement of a priori conditions分类
信息技术与安全科学引用本文复制引用
卜丽静,陈香雪,张正鹏,吴俊..基于两阶段残差条件扩散网络的遥感图像超分辨重建[J].液晶与显示,2025,40(11):1647-1660,14.基金项目
国家重点研究发展计划(No.2020YFA0713503) （No.2020YFA0713503）
湖南省教育厅湖南省普通高等学校教学改革研究项目(No.HNJG-20230279) （No.HNJG-20230279）
湖南省教育厅科研项目(No.23C0059) Supported by National Key Research and Development Program(No.2020YFA0713503) （No.23C0059）
Hunan Provincial Department of Education Hunan Provincial Teaching Reform Research Project for Regular Higher Education Institutions(No.HNJG-20230279) （No.HNJG-20230279）
Hunan Provincial Department of Education Scientific Research Project(No.23C0059) （No.23C0059）
