<p>Unmanned aerial vehicle (UAV)-borne gamma-ray spectrum survey plays 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 interpretations. 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 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$2\times$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>2</mn> <mo>×</mo> </mrow> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$4\times$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>4</mn> <mo>×</mo> </mrow> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$6\times$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>6</mn> <mo>×</mo> </mrow> </math></EquationSource> </InlineEquation> 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 <InlineEquation ID="IEq4"> <EquationSource Format="TEX">$2\times$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>2</mn> <mo>×</mo> </mrow> </math></EquationSource> </InlineEquation> magnification, significantly higher than 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.</p>

Super-resolution reconstruction of UAV-borne gamma-ray spectrum images based on Real-ESRGAN algorithm

期刊论文
发表日期：

2026年1月4日

查看全文

Xin Wang,
Yuan Yuan,
Xuan Zhao,
Guang-Hao Luo,
Qi-Qiao Wei,
He-Xi Wu,
Chao Xiong

摘要

Unmanned aerial vehicle (UAV)-borne gamma-ray spectrum survey plays 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 interpretations. 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 $2\times$ $2 \times$ , $4\times$ $4 \times$ and $6\times$ $6 \times$ 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 $2\times$ $2 \times$ magnification, significantly higher than 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.