## 1 Introduction
Rough determination of the value of the image length line \(d_{\Delta}\) (image width) allows us to start the activities related to the line length estimation with the given accuracy \(\Delta\), starting from \(10^{0}\) and ending e.g., at \(10^{-5}\). Practice shows that the accuracy at the level of \(10^{-5}\) allows for a significant improvement in the quality of the reconstructed image for a 30-100-fold summation of \(Sum_{MTL}\) of the replicas of the images obtained from the recorded emission signal revealing \(s(t)\). There remains the question of what measures and their criteria should be adopted, which would allow us to determine the correct value of the length \(d_{\Delta}\) of the image line, allowing for effective summation of image replicas. Effective summation is understood as a process that allows us to improve the image quality assessed, among others, by visual analysis and allowing to increase the value of the signal-to-noise ratio parameter described by the formula:  
\[SNR=10\log\biggl{(}\frac{Signal\_power}{Noise\_power}\biggr{)}. \tag{1}\]Initial considerations concerned the analysis of the possibility of using selected methods of assessing the contrast of digital images, based on the value of the average amplitude of the image pixels, on the maximum and minimum values of the image pixel amplitude, and the variance of gray levels [36,37]. Each of the methods is described by Equations (2), (8), and (11), respectively. 1. (a) evaluation of the contrast based on the value of the average amplitude of pixels of the reconstructed image \[Contrast_{I\_\_\Delta}=\frac{Contrast_{I\_\_\Delta}}{maximum_{I\_\_\Delta}},\] (2) where: \[Contrast_{I\_\_\Delta}=\frac{l_{\_\Delta\_max}-l_{\_\Delta\_min}}{\overline {l_{\_\Delta}}},\] (3) \[maximum_{\_\_\Delta}=\max(Contrast_{I\_\Delta}),\] (4) \[\overline{l_{\_\Delta}}=\frac{1}{N\cdot M}\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}l_ {\_\Delta}(n,m),\] (5) \[l_{\_\Delta\_\max}=\max_{\_\_\_\,n,m}(l_{\_\Delta}(n,m)),\] (6) \[l_{\_\Delta\_min}=\min_{\_\_\,n,m}(l_{\_\Delta}(n,m)),\] (7)  
\(M\)--columns number of reconstructed image;  
\(m\)--number of column of reconstructed image (\(m=0\), 1, 2,..., \(M-1\));  
\(N\)--rows number (lines) of reconstructed image;  
\(n\)--number of row (line) of reconstructed image (\(n=0\), 1, 2,..., \(N-1\));  
\(\Delta\)--accuracy of the line length \(d_{\_\Delta}\) estimation of the reconstructed image for which the contrast of the reconstructed image is calculated. In the carried out analyzes, \(\Delta=1.0\), 0.1, 0.01, 0.001, 0.0001 and 0.00001 were assumed;  
\(l_{\_\Delta}\)--value of image pixel amplitude for the line length \(d_{\_\Delta}\) calculated for accuracy \(\Delta\);  
\(l_{\_\Delta\_max}\)--the maximum value of the image pixel amplitude for the line length \(d_{\_\Delta}\) calculated for the accuracy \(\Delta\);  
\(l_{\_\Delta\_min}\)--the minimum value of the image pixel amplitude for the line length \(d_{\_\Delta}\) calculated for the accuracy \(\Delta\). 2. (b) evaluation of the contrast based on the maximum and minimum values of the amplitude of the pixel of the reconstructed image \[Contrast_{II\_\_\Delta\_\_\max}=\frac{Contrast_{II\_\Delta}}{maximum_{II \_\Delta}},\] (8) where: \[Contrast_{II\_\_\Delta}=\frac{l_{\_\Delta\_max}-l_{\_\Delta\_min}}{l_{\_ \Delta\_max}+l_{\_\Delta\_min}},\] (9) \[maximum_{\_\_\_}{II\_\Delta}=\max(Contrast_{II\_\Delta}),\] (10)
3. (c) evaluation of the contrast based on the variance of the gray levels of the reconstructed image \[Contrast_{I\_\_\Delta}=\frac{Contrast_{I\_\_\Delta}}{maximum_{I\_\_\Delta}},\]0 (11) where: \[Contrast_{I\_\_\Delta}=\frac{Contrast_{I\_\_\Delta}}{maximum_{I\_\_\Delta}},\]1 (12) \[Contrast_{I\_\_\Delta}=\frac{Contrast_{I\_\_\Delta}}{maximum_{I\_\_\Delta}},\]2 (13)The results of preliminary analyzes of the above methods showed that they are not suitable for the specific conditions related to the images reconstructed in the process of electromagnetic infiltration [38, 39, 40, 41].