Spatial-frequency Dual-Domain Feature Fusion Network for Low-Light Remote Sensing Image Enhancement

arXiv:2404.17400v1 Announce Type: new
Abstract: Low-light remote sensing images generally feature high resolution and high spatial complexity, with continuously distributed surface features in space. This continuity in scenes leads to extensive long-range correlations in spatial domains within remote sensing images. Convolutional Neural Networks, which rely on local correlations for long-distance modeling, struggle to establish long-range correlations in such images. On the other hand, transformer-based methods that focus on global information face high computational complexities when processing high-resolution remote sensing images. …

arxiv cs.ai cs.cv domain eess.iv feature fusion image light low network sensing spatial type