LCTCANet: a lightweight CNN-transformer context-aware network for remote sensing super-resolution

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.