A Review on Super-Resolution Reconstruction of Single-Frame Remote Sensing Images via Diffusion Models.
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| Title: | A Review on Super-Resolution Reconstruction of Single-Frame Remote Sensing Images via Diffusion Models. |
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| Authors: | Cao, Haoran1,2,3 (AUTHOR), Tan, Zheng1,2,3 (AUTHOR), Zhu, Baoyu1,2,3 (AUTHOR), Xiong, Huolin1,2,3 (AUTHOR), Lv, Qunbo1,2,3 (AUTHOR) lvqunbo@aoe.ac.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1702. 36p. |
| Subjects: | Remote-sensing images, Probabilistic generative models, Research methodology, High resolution imaging, Benchmark problems (Computer science), Algorithms, Image reconstruction |
| Abstract: | Highlights: What are the main findings? This paper systematically elaborates on the unique characteristics that distinguish remote sensing images from natural images, innovatively identifies the feature domain shift problem when natural image feature extraction methods are applied to remote sensing images, and provides a theoretical basis for conducting research on diffusion model-based super-resolution reconstruction algorithms tailored to remote sensing images. From the three perspectives of model architecture adaptation, training paradigm innovation and prior knowledge fusion, this paper comprehensively sorts out and refines the single-frame remote sensing image super-resolution algorithms based on diffusion models. It also integrates mainstream datasets and multi-dimensional evaluation metrics to construct a unified validation benchmark, thus providing adequate reference for the research on remote sensing image super-resolution reconstruction. What are the implications of the main findings? The unique characteristics of remote sensing images and the three core technical approaches analyzed in this paper provide a well-defined technical framework for subsequent research, which contributes to advancing the development of diffusion models in the field of remote sensing super-resolution reconstruction. The four future development directions proposed in this paper in response to current technical bottlenecks offer clear theoretical support and technical guidance for the subsequent research and practical application of diffusion models in the field of remote sensing super-resolution. Single-frame remote sensing image super-resolution can improve the spatial resolution of imaging without requiring a change in hardware, making it an important research direction in remote sensing. In recent years, owing to their excellent generative capability and training stability, diffusion models have shown great potential in the field of super-resolution remote sensing imaging. Given the rapid development of research in this field, it is necessary to conduct a comprehensive review of existing diffusion model-based super-resolution algorithms for remote sensing to help researchers accurately grasp the technical context and development trends. This paper surveys the application of diffusion models to single-frame SR remote sensing imagery. First, we elaborate on the intrinsic characteristics of remote sensing images from three perspectives—the imaging physics process, semantic features, and data scale and task objectives—thereby laying a tailored foundation for algorithm design. Subsequently, we categorize existing algorithms based on three core technical approaches: model architecture optimization, training paradigm innovation, and prior-knowledge fusion. For a comprehensive and objective evaluation of current algorithms, we compile mainstream datasets and multi-level evaluation metrics applicable to super-resolution remote sensing image tasks. Finally, combining the current technical bottlenecks, we propose four future development directions, aiming to provide theoretical support and technical guidance for the development of diffusion models in the field of super-resolution remote sensing imaging. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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