Improving gamma-ray spectral data accuracy with image restoration techniques for UAV applications

Image restoration technology has made significant progress in the fields of signal processing, computer vision, and remote sensing. However, the application of Unmanned Aerial Vehicle-borne gamma-ray spectrometry data processing is relatively limited. The aim of this study is to fully utilize the wide applicability of image restoration technology and explore its potential application in the processing of UAV-borne gamma-ray spectrum data. By using methods such as median filtering and edge influence compensation, targeted noise in UAV-borne gamma-ray spectrum data is processed, and data fusion is achieved through image restoration technology, significantly improving the accuracy and visualization effect of data reconstruction. The experimental results show that in practical applications, by fitting the mean vector of the original and restored images, the element content or count rate values can be successfully solved, and the generated contour map is basically consistent with the map trend. The useful information in the restored image is significantly increased, while the average variation value of interference information decreased by 41.47%.