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.

Real-time 2D imagery super-resolution (SR) in UAV remote sensing encounters significant speed and resource-consuming bottlenecks during large-scale processing. To overcome this, we propose Semantic Injection State Modeling for Super-Resolution (SIMSR), an ultra-lightweight architecture that integrates land-cover semantics into a linear state-space model. This integration mitigates state forgetting inherent in linear processing by linking hierarchical features to persistent semantic prototypes, enabling high-fidelity image enhancement. The model achieves a state-of-the-art PSNR of 32.9+ for 4x SR on RSSCN7 agricultural grassland imagery. Furthermore, the implementation of geographically-chunked (tile-based) parallel processing simultaneously eliminates computational redundancies, yielding a 10.85x inference speedup, a 54% memory reduction, and an 8.74x faster training time. This breakthrough facilitates practical real-time SR deployment on UAV platforms, demonstrating strong efficacy for ecological monitoring applications by providing the detailed imagery essential for accurate analysis.

Magnetic Resonance Imaging (MRI) Super-Resolution (SR) addresses the challenges such as long scan times and expensive equipment by enhancing image resolution from low-quality inputs acquired in shorter scan times in clinical settings. However, current SR techniques still have problems such as limited ability to capture both local and global static patterns effectively and efficiently. To address these limitations, we propose Delta-WKV, a novel MRI super-resolution model that combines Meta-in-Context Learning (MiCL) with the Delta rule to better recognize both local and global patterns in MRI images. This approach allows Delta-WKV to adjust weights dynamically during inference, improving pattern recognition with fewer parameters and less computational effort, without using state-space modeling. Additionally, inspired by Receptance Weighted Key Value (RWKV), Delta-WKV uses a quad-directional scanning mechanism with time-mixing and channel-mixing structures to capture long-range dependencies while maintaining high-frequency details. Tests on the IXI and fastMRI datasets show that Delta-WKV outperforms existing methods, improving PSNR by 0.06 dB and SSIM by 0.001, while reducing training and inference times by over 15\%. These results demonstrate its efficiency and potential for clinical use with large datasets and high-resolution imaging.