Super-Resolution Reconstruction of UAV-borne Gamma-Ray Spectrum Images Based on Real-ESRGAN Algorithm

Abstract: Unmanned aerial vehicle (UAV)-borne gamma-ray spectrum surveys play a crucial role in geological mapping, radioactive mineral exploration, and environmental monitoring. However, raw data are often compromised by flight and instrument background noise, as well as detector resolution limitations, which affect the accuracy of geological interpretation. This study aims to explore the application of the Real-ESRGAN algorithm in the super-resolution reconstruction of UAV-borne gamma-ray spectrum images to enhance spatial resolution and the quality of geological feature visualization. We conducted super-resolution reconstruction experiments with 2×,4×, and 6× magnification using the Real-ESRGAN algorithm, comparing the results with three other mainstream algorithms (SRCNN, SRGAN, FSRCNN) to verify the superiority in image quality. The experimental results indicate that Real-ESRGAN achieved a structural similarity index (SSIM) value of 0.950 at 2× magnification, significantly higher than that of the other algorithms, demonstrating its advantage in detail preservation. Furthermore, Real-ESRGAN effectively reduced ringing and overshoot artifacts, enhancing the clarity of geological structures and mineral deposit sites, thus providing high-quality visual information for geological exploration.