## 4 Discussion  
We consider the performance of the values \(\overline{I}_{n},\Sigma_{n}\) depending on the parameters \(\overline{T}_{nm},\Sigma_{nm}\). It is quite evident that when \(\overline{I}_{n}+3\sigma_{n}<I_{max}\) there will be the correlation \(\overline{I}_{n}\approx\overline{I}_{nm}\). Indeed, according to the \"three sigma\" criterion, 99.73% of all actual frame intensity values will be equal to the corresponding measured intensity values. We can conclude now that as the dispersion \(\sigma_{n}\) decreases (if \(\overline{I}_{n}<I_{max}\)), the adjustment for the effect considered will decrease and vice versa. In this case, when the dispersion \(\sigma_{n}\) is fixed, the growth in the intensity \(\overline{I}_{n}\) results in the adjustment upswing (the parameter \(b_{nm}\) upswing) and vice versa. In the limiting case when \(\overline{I}_{n}<I_{max}\) and \(\sigma_{n}\to 0\) or, equivalently, \(\sigma_{nm}\to 0\), we obtain \(\overline{I}_{n}\rightarrow\overline{I}_{nm}\). These dependencies also directly result from the solution of System (5). We now consider the case in which \(\overline{I}_{n}>I_{max}\). In this case, the difference between the measured spectral line intensity and the actual line intensity is higher than when \(\overline{I}_{n}<I_{max}\). This happens because, with the increase in \(\overline{I}_{n}\) in each pixel, some of the values \(I_{nk}>I_{max}\) (\(k=1,2\ldots,M\)) when measured are unaffected by \(\overline{I}_{n}\) and are replaced by \(I_{max}\). The greater the frame dispersion \(\sigma_{n}\) and the closer \(\overline{I}_{nm}\) to \(I_{max}\), the greater the difference between \(\overline{I}_{n}\) and \(\overline{I}_{nm}\). This is illustrated by the data in Figures 3 and 4, which show the dependence of value \(b_{n}\Big{(}\frac{1}{\alpha\,nm},\Sigma_{n}\Big{)}\) for a single pixel. It can be seen that when the relative actual by-frame dispersion is less than 10%, the adjustment is negligibly small if \(\overline{I}_{n}<0.95\cdot I_{max}\). At the same time, if the dispersion \(\Sigma_{n}=\frac{\sigma_{n}}{\overline{I}_{n}}\) is up to 40%, the adjustment can be neglected only if \(\overline{I}_{n}<0.75\cdot I_{max}\). It should be noted that the actual dispersion \(\sigma_{n}\) is wider than the measured \(\sigma_{nm}\). The reasons for this are discussed above. Apparently, when \(\overline{I}_{n}\) (and therefore \(\overline{I}_{nm}\)) increases, this difference becomes greater. Figures 5 and 6 represent the dependences \(b_{nm}\Big{(}\frac{1}{\alpha\,nm},\Sigma_{nm}\Big{)}\), and Figures 7 and 8 show \(\Sigma_{n}\big{(}\overline{I}_{nm},\Sigma_{nm}\big{)}\). As we predicted, as the parameter \(\alpha_{nm}=\frac{I_{max}}{I_{nm}}\) increases and the relative dispersion of the measured intensity increases, the parameter \(b_{nm}\) increases. The same can be said of the value \(\Sigma_{n}\). After comparison of the data from Figures 3 and 5, we can conclude that, at the same parameter \(\alpha_{nm}\), the in-equation \(b_{nm}\Big{(}\frac{1}{\alpha\,nm},\Sigma_{nm}\Big{)}>b_{nm}\Big{(}\frac{1}{ \alpha\,nm},\Sigma_{n}\Big{)}\) is satisfied. As we stated previously, this results from the fair correlation \(\Sigma_{n}>\Sigma_{nm}\). Above, we discussed the effect of reducing the measured intensity for a single pixel. When we analyze how this effect interferes with the spectral line intensity measurement, we must remember that the intensity adjustment in different pixels will be different. To illustrate the developed algorithm, we consider the situation in which the spectral line according to the measurements has a Doppler shape. We want to emphasize that the specific form of the measured spectral line intensity in this case is of no crucial significance. We can state that the half-width of the actual spectral line will be narrower, and the width at the base will be the same. This is true if, during the signal accumulation time in each pixel, the accumulated charge does not affect the neighboring pixels.