Proposed framework demonstrates 10.85x speedup in imagery super-resolution using UAV remote sensing, indicating enhanced deployment for ecological monitoring.
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 enables high-fidelity, real-time image enhancement. SIMSR mitigates state forgetting inherent in linear processing by linking hierarchical features to persistent semantic prototypes. The model achieves state-of-the-art performance, including a PSNR of 32.9+ for 4x SR on RSSCN7 agricultural grassland imagery. Furthermore, geographically-chunked (tile-based) parallel processing simultaneously eliminates computational redundancies, which yields 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.
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Lu et al. (2025) studied this question.
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