###### Abstract
The result of this analysis is an image that should not contain repeating graphic elements observed in a horizontal line. However, the exact indication of the number of image lines must already be carried out in an automatic manner, which allows for a quick classification of the recorded undesirable emissions. At the same time, this process, together with the algorithm for determining the correct length d of the image line with an accuracy of \(\Delta\) equal to at least \(10^{-5}\), enables automation of the process of recreating graphic data in the form of images with the possibility of effective further summation of tens of realizations of the same image (Figure 4). The method of pseudo-colouring of images was used to visualize the data. This made it possible to present the acquired data in the form of colour images, for which visual perception allows for the perception of more details than for images in grey colours [20; 32]. Thus, the use of the pseudo-colouring algorithm facilitates the analysis and classification of revealing emissions. Figure 2: An example of anechoic chamber. Incorrect determination of the number lines of image makes it impossible to effectively use image processing methods to improve its quality. In particular, it concerns the coherent summation algorithm of tens implementations of the same image (Figure 5). One of the measures used in the assessment of image quality is the one related to the contrast of the analysed image [33]. We can mention here the measures based directly on the values of the maximum and minimum pixel amplitudes, the values of the average pixel amplitudes or the variance of the pixel amplitudes. The fulfilment of the appropriate criterion, i.e., the maximum value of these measures, clearly indicates the correct number \(B\) of the line for which the appropriate values are calculated [28]. It should be noted, however, that the change in the number of B lines does not change the amplitudes of the pixels composing the image in the case of measures related to the contrast assessment. It only increases the number of pixels that are included in the calculation of each measure. Thus, it does not change the image quality and the contrast assessment. Hence, direct use of the measure of image contrast becomes ineffective. It also indicates the necessity to:  
* Propose another dedicated measure and its criterion, effective in correctly determining the number of lines of the reconstructed image from the registered revealing broadcast signal;
* Pre-processing the reconstructed image depending on the B number of lines, and then analysing it in accordance with the adopted measure. Figure 3: Illustrating the effect of the number lines of image on its content: (**a**) correct number lines of the image, (**b**) too few lines of the image, (**c**) too many lines of the image. The work on images summed up several times is proposed, assuming the use of a previously determined, correct value of the \(d_{\Delta}\) line length of the reconstructed image (\(\Delta\)--accuracy of the image line length estimation, where \(\Delta=10^{0},\ 10^{-1},\ 10^{-2},\ 10^{-3},\ 10^{-4}\), \(10^{-5}\)). Then, the maximum contrast should be achieved for the number \(B_{Corr}\) line of the  
Figure 4: The working scheme of coherent summing of images in electromagnetic infiltration process. Figure 5: Illustrating the influence of a wrongly determined number lines of image on the result of coherent summation (a summation of two first images): (**a**) for a small number lines of image, (**b**) for a large number lines of image. image corresponding to the number of the original image (Figure 6). This approach causes the pixel amplitude values to change by averaging them, and the maximum average value should be achieved for the correct number of image lines. Additionally, three other measures have been proposed to enable the determination of the number of lines of the reproduced image. These are measures based on the methods of determining the correct length d of the image line.