## 3 Results and Discussion
### Image Quality Assessment
#### 3.1.1 Overall Quality Assessment of Raw GPR Images
This proves the existence of highly differentiated structures and details that are present in the analyzed images. Images that are characterized by low entropy values (channels 11, 26, and 15) are characterized by the homogeneity and low diversity of structures. The image acquired from channel 22 is characterized by a high value of entropy, which proves a large amount of information and the heterogeneity and complexity of the structures represented in the radargrams. Higher entropy values are also characterized by higher PIQE values. This points not only to the heterogeneity of the image, but also to the presence of noise, which translates into a larger number of degraded blocks into which the input image was divided. Figure 12 presents sample resultant images of detection for extreme values of the quality of these radargrams, along with the assessment of detection effectiveness. One of the images is characterized by a high degree of blurring, which, in such a case, leads to a lower effectiveness of detection (M = 35%) than for the image acquired from channel 25 (M = 44%). Images with a lower degree of blurring are characterized by higher values of the PIQE indicator. Images with higher entropy values have a lower number of undetected objects being underground utility networks (n\({}_{\text{L2}}\)). Higher values of the Laplacian variance of the image lead to a larger number of all objects detected in the input image (considering the number of objects that are utility networks and those that are not--n\({}_{\text{L1}}\), n\({}_{\text{L2}}\)). The higher the value of the Laplacian variance, the more blurred the image. This indicates a larger number of undetected objects being underground utility networks in the input image (n\({}_{\text{L2}}\)). Images with a lower degree of blurring are characterized by higher values of the PIQE indicator. All non-reference coefficients (NIQE, BRISQUE, PIQE) were analyzed for their workings, capabilities, and limitations in the context of evaluating the quality of radargrams acquired from multichannel GPR. * The NIQE indicator is based on the construction of a quality-aware collection of statistical features that depend on a simple and successful space domain natural scene  
Figure 11: Venn diagram presenting the image selection results for route 3 based on the indicators entropy, PIQE, and Laplacian variance (green numbers indicate higher image quality, while red ones mark lower image quality). statistic (NSS) model. These features are derived from an entity of natural, undistorted images [58; 59]. * The operating principle of the PIQE algorithm consists in dividing the image into smaller regions, where the level of noise and artifacts is analyzed locally. Areas characterized by large artifacts are identified as low-quality areas. The PIQE algorithm consists in dividing the image into blocks (e.g., of the size of 8 \(\times\) 8, 16 \(\times\) 16, 32 \(\times\) 32). The value of the PIQE index is calculated based on the number of degraded blocks and all blocks within the image. Dividing the image into smaller blocks gives a better assessment of quality than the NIQE or BRISQUE index [58; 59]. * The BRISQUE indicator compares the image with a default model generated from images of natural scenes with similar distortions. Radargrams do not represent natural scenes. Therefore, the BRISQUE indicator, based on the statistics of natural images, will not reliably assess the quality of GPR-acquired images.