Ultra-lightweight semantic-injected imagery super-resolution for real-time UAV remote sensing

Abstract 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 . Journal vv (yyyy) aaaaaa Author et al (Fig. 1), particularly enhancing extremely efficient UAV-relevant metrics on the RSSRD-SE dataset. This enables deployable high-precision SR for ecological monitoring on resource-constrained platforms. 2 Related Work 2.1 Conventional and Convolution-based Techniques Traditional interpolation methods (e.g., bicubic) lack semantic understanding despite computational efficiency [26]. Deep learning-based approaches have since dominated, with SRCNN [27] pioneering the use of convolutional layers for local patch extraction and HR reconstruction. VDSR [28] addresses this via deeper residual networks, expanding receptive fields to capture hierarchical features-pixel-level edges in early layers and regional semantics in deeper ones. However, CNNs grapple with global-local modeling trade-offs and computational bottlenecks. CNNs' local receptive fields hinder long-range correlation capture. These limitations drive the attention mechanisms for effective global-local collaboration in remote sensing super-resolution.