## 3 Results  
Table 1 presents metrics used to verify the reliability of the proposed CNN-based SR model. Specifically, we calculated the Peak Signal to Noise Ratio (PSNR) and the Structural Similarity Index Metric (SSIM), often used for evaluation of SR solutions. In Table 2 we collected values of RR estimated from thermal image sequences. In both tables we present results for data after resolution degradation and enhancement. Down-scaling and then up-scaling of the image with inverse scales, i.e., 1/2 and 2 resulted in resolution degradation, producing the synthesized low-resolution (LR) output of the same size as the input image. In case of SR, after generating LR images they were enhanced with the proposed DL model. Enhancing of breathing signal was additionally performed by applying EVM--the color magnification algorithm. Results of RR estimation from sequences enhanced with EVM are also available in Table 2. Results of RR above 50 were treated as errors and not taken into account for RMSE calculation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scaling Factor**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Bit Resolution**} & \multicolumn{2}{c}{**Evaluation Metrics on the Test Set**} \\ \cline{3-5}  & & & **PSNR** & **SSIM** \\ \hline \multirow{4}{*}{1/2 and 2} & \multirow{2}{*}{**Lepton Bicubic**} & 8 bits & 41.82\(\pm\) 0.55 & 0.96\(\pm\) 0.01 \\ \cline{3-5}  & & 16 bits & 63.82\(\pm\) 0.73 & 0.99\(\pm\) 0.01 \\ \cline{3-5}  & \multirow{2}{*}{**Lepton SR**} & 8 bits & **43.21\(\pm\) 0.33** & **0.97\(\pm\) 0.01** \\ \cline{3-5}  & & 16 bits & **72.92\(\pm\) 4.47** & **0.99\(\pm\) 0.01** \\ \hline \multirow{4}{*}{1/4 and 4} & \multirow{2}{*}{**Lepton Bicubic**} & 8 bits & 39.91\(\pm\) 0.46 & 0.81\(\pm\) 0.11 \\ \cline{3-5}  & & 16 bits & 61.31\(\pm\) 0.69 & 0.99\(\pm\) 0.01 \\ \cline{3-5}  & \multirow{2}{*}{**Lepton SR**} & 8 bits & 42.18\(\pm\) 0.24 & 0.95\(\pm\) 0.01 \\ \cline{3-5}  & & 16 bits & 67.34\(\pm\) 5.83 & 0.99\(\pm\) 0.02 \\ \hline \multirow{4}{*}{1/2 and 2} & \multirow{2}{*}{**SC3000 Bicubic**} & 8 bits & 42.69\(\pm\) 3.36 & 0.81\(\pm\) 0.22 \\ \cline{3-5}  & & 16 bits & 68.98\(\pm\) 1.02 & 0.99\(\pm\) 0.01 \\ \cline{3-5}  & & 8 bits & 43.61\(\pm\) 0.18 & 0.96\(\pm\) 0.01 \\ \cline{3-5}  & & 16 bits & **70.05\(\pm\) 0.91** & **0.99\(\pm\) 0.01** \\ \hline \multirow{4}{*}{1/4 and 4} & \multirow{2}{*}{**SC3000 Bicubic**} & 8 bits & 41.36\(\pm\) 2.30 & 0.79\(\pm\) 0.21 \\ \cline{3-5}  & & 16 bits & 65.74\(\pm\) 0.95 & 0.99\(\pm\) 0.01 \\ \cline{3-5}  & & 8 bits & **43.97\(\pm\) 0.22** & **0.96\(\pm\) 0.01** \\ \cline{3-5}  & & 16 bits & 66.50\(\pm\) 0.93 & 0.99\(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) for sequences downscaled with bicubic interpolation and then enhanced with the proposed SR DL model using scale of 2 and 4. Best result for each dataset was marked in **bold** (separately for 8 and 16-bit data). Figure 5: Example of frames extracted from the Lepton sequences enhanced with EVM algorithm. **Top**: original sequence, **Bottom**: enhanced sequence. Qualitive results of applying each algorithm on sequences from both datasets are visualized in Figures 6 and 7. The restoration of facial features with the proposed model from images downscaled 2 and 4 times is presented in the last frame and the third to last frame, respectively in both figures. The image enhancement is especially visible for the Lepton dataset which consist of images of very small spatial resolution (80 \(\times\) 60). One can notice that the LR image created for the scale 4 (second to the last image in Figure 6) is almost completely blurred, while its enhanced version (the last image) contains most of the detailed components and is very similar to the original HR input (the first image).