# Fusing Ground-Penetrating Radar Images for Improving Image Characteristics Fidelity
## 5 Methodology and Experimental Results
(**b**) Binary pattern extraction from a hyperbolic response, which will serve as the initial input for the subsequent LBP analysis from the backward scan. analyze the components of each new binary pattern and obtain their magnitude spectrum that demonstrates the dominant frequencies, which correspond to structural features and edges in the spatial domain. Therefore, because excessive patterns create specific frequency components, taking the magnitude spectrum makes them easier to identify and analyze. A circular mask is also applied on the spectrum amplitude to obtain the frequency elements closest to dc, as they contain the most important structural information, helping to focus on the reconstruction of the pattern and to identify the remaining redundant patterns as well. Upon completion of stage 1 of the methodology, DC element hiding is performed, where pixels inside a specified radius in the domain of the spectrum amplitude are set to zero. This operation effectively removes or reduces the effect of the DC component in the subsequent processing stages, which represents the average intensity or average baseline of the image. By removing the DC component, we focus more on the variations and details present in the frequency content of the image, improving the resolution of specific patterns. For the sake of convenience, only a small part of the scan is presented at this intermediate stage in Figure 10.  
Thus, by combining components from the magnitude spectrum of the extracted pattern and the phase information of the original image, it is ensured that the phase information they contain and the critical spatial information are preserved in the resulting image. Since the aim of the method is to detect as best as possible the excessive patterns throughout the image and to view the reconstructed one to distinguish them or not, the inverse Fourier transform is applied, and its absolute value is obtained. This method enhances the visibility of subsurface features in GPR data, preserving the spatial information that is vital for the accurate reconstruction of their spatial arrangement. The masking that is applied helps to filter out unwanted high-frequency noise that can alter important features of the underground space. The process is completed by summing up the two resulting images to better visualize the localized over-response pattern after the filtering process, which was created by capturing and then composing the two scans of the same path but opposite scan directions (Figure 11). Figure 10: (**a**) A reconstructed image, after masking and pattern recognition, highlighting the most powerful hyperbolic responses, and providing a detailed and accurate representation. (**b**) The same applies to the other image as well. If the summation is performed all over the entire length of the image, the obtained reconstructed image will be as shown in Figure 12. The combination of two GPR images has the advantage of improving the SNR by \(\sqrt{2}\) = 1.41. This is because the signal remains the same while the standard deviation of the noise decreases by \(\sqrt{2}\) (2 is the number of images). Finally, the image obtained after the summation is further analyzed using the LBP technique to extract detailed textural information, as illustrated in Figure 13. Thus, this information complements the frequency domain analysis performed earlier. While the Fourier transform emphasizes periodic structures and dominant frequencies, LBP provides detailed textural information, enabling a more comprehensive analysis since it captures local variations in pixel intensity by emphasizing edges and boundaries and enhancing the visibility of exaggerated patterns. The application of the LBPs is more attractive than co-occurrence matrixes or other 2D statistical-textual characteristics since the mathematical burden is small and the threshold selection is flexible. Figure 11: A part of the result of the summation of the two images, created after applying masking and fusion techniques, effectively highlighting the most powerful hyperbolic responses.