Ensemble Image Super-Resolution for Uav Geo-Localization

In this paper, we address the challenge of visual geo-localization from low-quality UAV imagery captured in real world environments. We propose a two-stage architecture, which includes Super-Resolution and visual geo-localization. We introduced novel, non-learnable Ensemble Super-Resolution (ESR) module, which first refines upscaled aerial frames, then seamlessly feeds the enhanced imagery into a visual geo-localization pipeline. Designed as a parallelizable block integrated directly into any SR computation graph, ESR combines classical Bicubic interpolation with neural SR models – boosting image fidelity and overall system accuracy without additional training and executing efficiently on most hardware accelerator. We validate our approach on a dataset of 37 000 real-world UAV images, each downscaled by a factor of four and then restored via baseline methods (Bicubic, Bilinear, Nearest Neighbour, DRCT, HMA, HAT, SwinFIR) as well as our ESR-enhanced pipeline. Quantitative evaluation shows that standalone Super-Resolution methods yield PSNR in the low 20s dB and SSIM of 0.6–0.7 – far below standard benchmarks-leading to a marked drop in geo-localization accuracy (Recall@1 and AP). In contrast, our ESR module stabilizes SR outputs and recovers image fidelity, raising geo-localization Recall@1 to 87.0 % (vs. 84.96 % with HMA restoration) and AP to 89.1 % (against 87.41 % with HMA restoration). Our contributions are: Two-stage framework combining Image Super-Resolution and visual geo-localization approaches tailored for low- resolution, noisy UAV data. Non-learnable, parallelizable ESR block that fuses Bicubic interpolation with neural restoration within the network Super-Resolution graph – requiring no retraining and fully compatible with most accelerator. Comprehensive empirical study demonstrating that ESR substantially narrows the domain gap and boosts geo- localization performance in real-world conditions. We conclude that embedding lightweight, hardware-agnostic ensemble strategies into SR pipelines is a promising direction for robust UAV-based visual localization. Future work will explore adaptive ensemble weighting and domain- aware SR architectures to further mitigate aerial imaging noise and variability.