Open access
Aug 2026
EEMSAGAN: edge-enhanced multi-scale attention generative adversarial network for remote sensing image super-resolution
This paper develops a dual-path generator architecture: the standard path thoroughly extracts deep high-level features, while the auxiliary path preserves prior information and high-frequency details, and introduces a multi-scale feature extraction module to boost the model’s ability to capture features at various scales.
Longjie Wang, Dandan Huang, Ningjuan Ruan et al.
· Scientific Reports · 0 citations