## 3 Evaluation of Raster Parameters  
The quality of the reconstructed image can be assessed on the basis of both subjective (observational) and objective (computational) methods. Each of them gives a result that may be acceptable in the process of reading the graphic data contained in the reconstructed images. However, reading the data does not always end further image processing. Very often the images contain a number of disturbances that should be eliminated using various methods. One of them is the coherent summation of several realizations of the same image. In this case, the evaluation result of the subjective quality of the reconstructed single image very often becomes insufficient for the effectiveness of the coherent summation method in improving the quality of the reconstructed image. The reason is incorrectly determined raster parameters, i.e., the length of the image lines and the number of lines forming the image. In this case, computational metrics or objective methods may prove useful. The authors proposed three different methods to estimate the line length of the reconstructed image. The effectiveness of each of the methods was demonstrated on the basis of the analyzes carried out with the use of the recorded actual revealing emission signals, the sources of which were test images showing various structures of the processed data. Each of the proposed methods is based on the input data \(D_{Ent}\), which is the initial value of the line length of the image. It is assumed that the \(D_{Ent}\) value of the image line length d is indicated by a prior analysis that uses the Chirp-Z transform algorithm or the periodicity analysis of the recorded revealing emission signal [52; 53]. At a later stage, it is necessary to estimate the \(d_{\Delta}\) parameter more precisely. This process begins with the estimation of the \(d_{\Delta}\) value with the accuracy of \(\Delta=1.0\). For this purpose, the values of the proposed measures are calculated for methods I, II, and III, for the length \(d_{1}\) of the line according to the relationship:  
\[d_{\Delta}=D_{Ent}-(10\!\cdot\!\Delta-n\!\cdot\!\Delta) \tag{14}\]  
where:  
\(\Delta=1.0\);  
\(n=0,1,2,\ldots,20\);  
\(D_{Ent}\)--image line length value estimated according to a previous step (e.g., Chirp-Z transform). Meeting the criteria for the proposed measures allows to estimate the length \(d_{\Delta}\) of the line for a given accuracy. Then the accuracy of the estimation is increased according to the relationship:  
\[d_{\Delta}=d_{\Delta\cdot 10}-(10\!\cdot\!\Delta-n\!\cdot\!\Delta) \tag{15}\]  
where:  
\(\Delta\)--the accuracy of the image line length estimation. Figure 6: Image reconstructed by summing 30 images for the line length \(d=1920.001\). \(d_{\Delta\Delta 10}\)--the line length estimated with a given accuracy \(\Delta\) meeting the adopted criterion defined below. Meeting the appropriate criterion allows to indicate one of the \(d_{\Delta}\) values calculated in the interval \([d_{\Delta 10}-10\cdot\Delta;d_{\Delta 10}+10\cdot\Delta]\). Three methods have been proposed:  
* (a) method I--based on the minimum value of the sum of the differences between the maximum and minimum amplitudes calculated for the individual vertical lines of the reconstructed image. * (b) method II--based on the maximum value of the difference of the maximum and minimum values of the sum of the pixel amplitudes calculated for the individual vertical lines of the reconstructed image. * (c) method III--based on the minimum value of the sum of the maximum pixel amplitudes calculated for the individual vertical lines of the reconstructed image. For each of the methods, a criterion was adopted related to the achievement of the required value by the calculated quantity. In the case of methods, I and III, it is the minimum calculated for the \(d_{\Delta}\) value determined by relations (14) and (15) for the given accuracy \(\Delta\), while for method II it is the maximum for analogous conditions.