## 2 Materials and Methods
### Evaluation of Results  
The image conversion method was tested with the use of a second database that uses the original database. The resolution of the original images is high and only a fragment of the image was used in the learning process. Other parts of the original images were used for testing. The learning criterion is MSE, but the use of image quality metrics is better to judge final image conversion. There are many image quality metrics, both objective and perceptual (subjective) [40; 41; 42].  
One of the metrics is the SSIM index [43] (Structural Similarity Index Measure), which contains three internal indicators: the luminance term, the contrast term and the structural term. SSIM enables the weighing of individual components and the results show the overall SSIM (all weights equal to 1) and the SSIM only for the structure component (weights for luminance \(\alpha\) and contrast \(\beta\) terms equal to 0, weight for the structure \(\gamma\) term equal to 1). The following formula is used to calculate the SSIM index:  
\[SSIM(A,B)=\left[l(A,B)\right]^{\alpha}\cdot\left[c(A,B)\right]^{\beta}\cdot \left[s(A,B)\right]^{\gamma}, \tag{2}\]  
where, \(A\) and \(B\) are reference and tested images, \(l(.,.)\) is luminance function, \(c(.,.)\) is contrast function and \(s(.,.)\) is structure function.  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline
**No.** & **Name** & **Type** & **Activations** & **Learnables** \\ \hline
1 & **imageinput** & Image Input & \(\left[256\times 256\times 3\right]\) & - \\  & \(\left[256\times 256\times 3\right]\) images & & & \\ \hline
2 & **conv\_1** & Convolution & \(\left[252\times 252\times 128\right]\) & Weights \(\left[5\times 5\times 3\times 128\right]\) \\  & \(128\)\(\left[5\times 5\times 3\right]\) convolutions & & & Bias \(\left[1\times 1\times 128\right]\) \\  & with stride [1 1] and padding [0 0 0 0 ] & & & \\ \hline
3 & **relu\_1** & ReLU & \(\left[252\times 252\times 128\right]\) & - \\ \hline
4 & **conv\_2** & Convolution & \(\left[248\times 248\times 256\right]\) & Weights \(\left[5\times 5\times 128\times 128\right]\) \\  & \(256\)\(\left[5\times 5\times 128\right]\) convolutions & & & Bias \(\left[1\times 1\times 256\right]\) \\  & with stride [1 1] and padding [0 0 0 0 ] & & & \\ \hline
5 & **relu\_2** & ReLU & \(\left[248\times 248\times 256\right]\) & - \\ \hline
6 & **conv\_3** & Convolution & \(\left[244\times 244\times 256\right]\) & Weights \(\left[5\times 5\times 256\times 128\right]\) \\  & \(256\)\(\left[5\times 5\times 256\right]\) convolutions & & & Bias \(\left[1\times 1\times 256\right]\) \\  & with stride [1 1] and padding [0 0 0 0] & & & \\ \hline
7 & **relu\_3** & ReLU & \(\left[244\times 244\times 256\right]\) & - \\ \hline
8 & **conv\_4** & Convolution & \(\left[242\times 242\times 1\right]\) & Weights \(\left[3\times 3\times 256\right]\) \\  & \(1\)\(\left[3\times 3\times 256\right]\) convolutions & & & Bias \(\left[1\times 1\right]\) \\  & with stride [1 1] and padding [0 0 0 0 ] & & & \\ \hline
9 & **regressionoutput** & Regression & - & - \\  & \(\left[242\times 242\times 1\right]\) MSE & Output & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of layers.