## 1 Introduction  
The modern management of underground infrastructure requires an accurate and reliable determination of the location, type, and technical condition of elements of utility networks. As far as the safety of the conducted modernization works of the existing infrastructure is concerned, non-invasive detection is crucial for the implementation of BIMtechnology and 3D cadastre [1; 2; 3]. One of the non-invasive geophysical methods for the detection of various underground media and the assessment of their technical condition is the ground-penetrating radar (GPR) method. The operating principle of GPR is based on the emission of electromagnetic waves from the transmitting antenna of the radar. The emitted sinusoidal signal penetrates the borders between media and materials that have different electric properties (i.e., electric permittivity and conductivity). Part of the signal penetrates into the medium, while the remaining part is dispersed or reflected, which is recorded by the receiving antenna of the ground-penetrating radar [4; 5; 6; 7; 8]. The dielectric constant, also referred to as electric permittivity, is a value that is specific for any given material. Its value is mainly determined by the presence of water in the given sub-surface medium [9; 10; 11; 12; 13; 14]. The differences in the values of the electric permittivity of soil media and underground infrastructure allow for the detection of both metal and non-metal pipelines. Another factor that influences the ability to penetrate geological media is the attenuation of electromagnetic waves, whose value (the medium attenuation coefficient) increases in the vicinity of, for example, silty materials or salty water, as mentioned by [PERSON] et al. [15]. An important aspect of the analysis of GPR (ground-penetrating radar) data is the number of radargrams acquired [16; 17]. A high level of noise leads to the deterioration of image quality. Commonly used image quality factors are PSNR, contrast, brightness, focus, sharpness, and illumination [18; 19]. There are also a few studies on the quality for target detection, target tracking, and event detection for reconnaissance applications. In the work by [PERSON] and [PERSON] [20], the applicability of the National Imagery Interpretability Ratings Scale (NIIRS) to an automated target detection algorithm was examined, and it was found that NIIRS is not a good predictor of target detection performance. This is why objective quality inspection methods are highly desirable. Their main aim is to obtain good-quality images. High image quality helps image detection methods extract certain features and objects. Unfortunately, there are few studies that propose a comprehensive evaluation index to evaluate image quality, which was discussed by the authors [PERSON] and [PERSON] in [21]. The problems of the analysis and reduction in noise and interference that occur in GPR images has been the subject of research presented in many publications, such as by [PERSON] et al. in [22], and continues to pose a challenge in terms of the consequences and influence of these factors on the process of the detection and classification of underground objects. In multiple studies, the preliminary work begins with modeling noise, blur, and compression artifacts in order to address the blurriness, blockiness, graininess, and ringing in images [23; 24; 25; 26]. The noise and low quality of the acquired images are determined by multiple factors, whose sources may be roughly divided into environmental ones (dielectric heterogeneity, moisture, presence of other objects, e.g., rocks, roots, or hollow spaces), instrumental ones (equipment limitations or electromagnetic interference), and methodological ones (settings of the measurement parameters and calibration of the ground-penetrating radar).