Hui Liu, Yurong Qian, Xiwu Zhong, Long Chen, GuangQi Yang
Optical Engineering, Vol. 60, Issue 10, 100901, (October 2021) https://doi.org/10.1117/1.OE.60.10.100901
TOPICS: Remote sensing, Reconstruction algorithms, Image fusion, Super resolution, Image processing, Spatial resolution, Optical engineering, Lawrencium, Image quality, Data modeling
The super-resolution (SR) reconstruction of remote sensing images is a low-cost and efficient method to improve their resolution, and it is often used for further image analysis. To understand the development of SR reconstruction of remote sensing images and research hotspots and trends, we examined its history and reviewed existing methods categorized into traditional, learning-based, and deep-learning-based methods. To evaluate the reconstruction performance, we conducted experiments comparing various algorithms for the single- and multi-frame SR reconstruction of remote sensing images considering three datasets. The experimental results indicate the advantages and limitations of single- and multi-frame reconstruction, with the latter showing a higher performance. Finally, we provide directions for future development of this SR reconstruction.