## 2 Materials and Methods
### Non-Reference Quality Assessment Metrics  
#### 2.3.1 Quality Metrics  
This sub-section provides a description of the functioning and application of all the indicators used, i.e., entropy, PIQE, and Laplacian variance. These indicators were selected based on the criterion of their functioning in images with a very high degree of noise that, at the same time, contain a large number of artifacts (being both objects of interest and unidentified objects that cannot be classified as elements of utility networks). Based on the theoretical analyses and the conducted experiments, it was noted that the selected indicators also cooperate well with binary images, so they will be appropriate for the whole process of data processing and the process of characterizing and selecting specific radargrams. Entropy  
The entropy of an image is determined based on the distribution of probability of the intensity of pixels in the analyzed image. Assuming that \(p_{i}\) represents the probability of the occurrence of intensity \(i\) in the image, the value of entropy may be calculated from Equation (2):  
\[H_{N}=\frac{1}{L}\sum\limits_{i=0}^{2^{L}-1}p_{i}log_{2}\frac{1}{p_{i}}, \tag{2}\]  
where  
\(L\)--number of intensity values;  
\(p_{i}\)--probability of occurrence of the given intensity \(i\) in the image. Images with high entropy are characterized by a large number of details and a diversity of the structures represented in the image. In such images, the distribution of pixel intensity is wide. Low-entropy images are characterized by the homogeneity and low diversity of structures. In such cases, the values of pixel intensity fall into a narrow range. Low entropy indicates a small amount of information and a low level of image complexity as described by [PERSON], [PERSON] et al. in [55]. PIQE (Perception-Based Image Quality Evaluator)  
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 first stage is the calculation of the MSCN (mean subtracted contrast normalized) index, which allows to highlight local contrasts in the image by deducting the local average and normalization in reference to local variance [56; 57]. The value of the MSCN coefficient is expressed by Equation (3):  
\[MSCN(x,y)=\frac{I(x,y)-\mu(x,y)}{\sigma(x,y)+C}, \tag{3}\]  
where  
\(I(x,y)\)--value of the pixel (x,y);  
\(\mu(x,y)\)--value of the local average within the pixel (x,y);  
\(\sigma(x,y)\)--value of the local standard deviation within the pixel (x,y);  
\(C\)--value of the constant that prevents division by 0. Then, based on the determined values of MSCN, the algorithm identifies the blocks that are characterized by high and low local variance. In areas characterized by a low value of local variance, the algorithm analyses the standard deviation of pixel intensity from the average value--Equation (4):  
\[\sigma=\frac{1}{N}{\sum\limits_{i=1}^{N}{\sum\limits_{j=1}^{N}{\left|{p(i,j)-\mu }\right|}}}, \tag{4}\]  
where  
\(\mu\)--value of average intensity within the block;  
\(N\)--number of pixels in the given block. Higher values of standard deviation point to the presence of noise in the image. The algorithm also analyses the distinguished blocks of images in terms of the presence of Gaussian noise. The value of the PIQE index is calculated based on the number of degraded blocks and all blocks within the image. Lower values of PIQE indicate a higher quality of the image.