## 4 Discussion  
### Discussion of Results  
The paper shows that the preparation of learning data requires a thorough analysis of the acquisition process. Mechanical problems were identified and an algorithmic solution was proposed for compensation. The proposed solution confirms the possibility of converting an H&E color image to a UV virtual image. The random results presented in Figures 8 and 9 show a very high similarity between the real UV image (main PC) and that created by the neural network. It should be noted, however, that not all elements of the structure are mapped, for example in Figure 8c, the dark area in the center of the image. The output images have not undergone additional contrast correction as it may cause changes in image perception (an image with higher contrast is usually considered better). The neural network has the last layer of the regression type, so the mapping comparison should be without the contrast correction of the output image. The feature of UV images is higher optical resolution in relation to images in the visible light range. This means that the neural network can to some extent perform image enhancement operations in terms of improving resolution. Figure 10: Histograms of SSIM and SSIM (structure only) for training and testing samples (the red vertical line represents the average on histograms). In the subjective perceptual assessment of images, it seems that the network reproduces smaller than larger structures relatively well. This is visible as the loss of the brightness value of the images for larger uniform structures, for example the light areas in Figure 9c, or the dark areas in Figure 8b-c (sinusoids). Since the lattice is of the convolutional type, individual convolution operations have their range of influence both within one layer and within all. Increasing the size of convolution nuclei could improve the quality of work, but it would be necessary to increase their number and training time. The loss of the brightness values for larger areas means that the neural network behaves like a high pass or band pass filter. This does not mean that the low-frequency spatial component is completely removed. Figure 9c shows the differences between the original UV image (main PC) and the reconstruction. The neural network fringes around the edges (lipid droplets regions that have been dissolved during the tissue process, especially). Interestingly, the introduction of this type of artifact improves the visual quality of the image. This is typical of unsharp mask filters [46]. The reconstructed image is visually sharper than the original. This type of operation, although the picture seems to be better for humans, results in deterioration of the SSIM values. For this reason, an attempt to unambiguously assess the quality with the use of SSIM is not fully adequate. The use of general blind measures is also not correct because histological images have their own specificity. Since the neural network can correct images, measure values should be treated with caution. The study proposes the use of two quality assessment indicators: SSIM and SSIM (structure only). Both indicators were used to evaluate the results for both the training and testing images. The mean values of SSIM and SSIM (structure only) are close to 0.6 and 0.5, respectively. In the case of pairs of training images, they are obviously larger because the neural network is trained by them and there is some matching. As the neural network has problems with mapping the brightness of larger areas, it was decided to use the simplified SSIM only for the structure. The values for SSIM (structure only) are shifted on average by about \(+0.1\). This means that the neural network reproduces the structure relatively well. Achieving high SSIM values around 0.9-1.0 is in practice unlikely because one of the components of the original H&E and UV (RGB) images is noise and tissue granulation. Noise is acquired and can be reduced by longer exposure. Granulation is a feature of biological material because it contains many small structures.