Efficient remote sensing image super-resolution with residual-enhanced wavelet and key-value adaptation

Remote sensing image super-resolution (SR) is vital for urban planning, precision agriculture, and environmental monitoring, yet existing methods have limitations: CNNs with restricted receptive fields cause edge blurring, Transformers with O ( L 2 d ) complexity fail in gigapixel-scale processing, and SSMs (e.g., Mamba) have directional biases missing diagonal features. To address these issues, this study proposes the REW-KVA architecture, integrating three innovations: Residual-Enhanced Wavelet Decomposition for separating low/high-frequency features and suppressing noise; Linear Attention with Key-Value Adaptation (complexity O ( L d ) ) for global context modeling; and Quad-Directional Scanning for omnidirectional feature capture. Validated on five datasets (DFC 2019, OPTIMAL-31, RSI-CB, WHU-RS19, UCMD), REW-KVA achieves state-of-the-art PSNR (29.17 dB on DFC 2019, 31.08 dB on RSI-CB) and SSIM (0.8958 on DFC 2019, 0.9442 on RSI-CB). It reduces memory reads by 35%, parameters by 42% (vs. SwinIR), and processes 1024 × 1024 images in 0.47 s (3.2 × faster than SwinIR), serving as a deployable solution for resource-constrained platforms.