Enhancing Remote Sensing Image Super-Resolution with Efficient Hybrid Conditional Diffusion Model

doi:10.3390/rs15133452

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	Enhancing Remote Sensing Image Super-Resolution with Efficient Hybrid Conditional Diffusion Model
	L. Han; Y. Zhao; H. Lv; Y. Zhang; H. Liu; G. Bi and Q. Han
刊名	Remote Sensing
	2023
卷号	15 期号:13
ISSN号	20724292
DOI	10.3390/rs15133452
英文摘要	Recently, optical remote-sensing images have been widely applied in fields such as environmental monitoring and land cover classification. However, due to limitations in imaging equipment and other factors, low-resolution images that are unfavorable for image analysis are often obtained. Although existing image super-resolution algorithms can enhance image resolution, these algorithms are not specifically designed for the characteristics of remote-sensing images and cannot effectively recover high-resolution images. Therefore, this paper proposes a novel remote-sensing image super-resolution algorithm based on an efficient hybrid conditional diffusion model (EHC-DMSR). The algorithm applies the theory of diffusion models to remote-sensing image super-resolution. Firstly, the comprehensive features of low-resolution images are extracted through a transformer network and CNN to serve as conditions for guiding image generation. Furthermore, to constrain the diffusion model and generate more high-frequency information, a Fourier high-frequency spatial constraint is proposed to emphasize high-frequency spatial loss and optimize the reverse diffusion direction. To address the time-consuming issue of the diffusion model during the reverse diffusion process, a feature-distillation-based method is proposed to reduce the computational load of U-Net, thereby shortening the inference time without affecting the super-resolution performance. Extensive experiments on multiple test datasets demonstrated that our proposed algorithm not only achieves excellent results in quantitative evaluation metrics but also generates sharper super-resolved images with rich detailed information. © 2023 by the authors.
URL标识	查看原文
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/67515]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	L. Han,Y. Zhao,H. Lv,et al. Enhancing Remote Sensing Image Super-Resolution with Efficient Hybrid Conditional Diffusion Model[J]. Remote Sensing,2023,15(13).
APA	L. Han,Y. Zhao,H. Lv,Y. Zhang,H. Liu,&G. Bi and Q. Han.(2023).Enhancing Remote Sensing Image Super-Resolution with Efficient Hybrid Conditional Diffusion Model.Remote Sensing,15(13).
MLA	L. Han,et al."Enhancing Remote Sensing Image Super-Resolution with Efficient Hybrid Conditional Diffusion Model".Remote Sensing 15.13(2023).