LCTCANet: a lightweight CNN-transformer context-aware network for remote sensing super-resolution
TL;DR
LCTCANet, a lightweight CNN-Transformer hybrid architecture for remote sensing SR, integrates a core feature extraction module composed of a global contextual local block and an edge-structure fusion block that enables joint learning of global context and local detail.
Abstract
Remote sensing imagery is rich in textures and exhibits strong spatial autocorrelation, making both local and global feature extraction essential for super-resolution (SR). Although CNN-based SR methods effectively capture local patterns, their limited receptive field hinders long-range modeling, degrading reconstruction quality. Moreover, their high parameter counts and computational costs restrict deployment on resource-constrained platforms. To address these issues, we propose LCTCANet, a lightweight CNN-Transformer hybrid architecture for remote sensing SR. The network integrates a core feature extraction module composed of a global contextual local block (GCLB) and an edge-structure fusion block (ESFB). GCLB leverages multi-head self-attention to model global dependencies and enhance structural coherence. ESFB, by contrast, focuses on fine-grained local textures through edge-aware attention and structural refinement. Their fusion enables joint learning of global context and local detail, reinforcing high-frequency and structural information. Extensive experiments on NWPU-RESISC45, Draper, and UC Merced datasets demonstrate that LCTCANet achieves competitive SR performance while maintaining low computational cost and parameter efficiency.