# Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals
## 7 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Input** & **CS [\%]** & **MC** & **SRR** & **PSNR [dB]** & **MAE** & **N** & **M** & **SD** & **t(99)** & \(p\)** \\ \hline LR & 50 & NO & NO & 26.09 & 19.22 & 100 & 26.09 & 0.03 & \(-1.252\) & 0.213 \\ \hline LR & 50 & YES & NO & 27.93 & 18.43 & 100 & 27.93 & 0.03 & \(-1.075\) & 0.285 \\ \hline HR & 50 & YES & NO & 29.38 & 16.59 & 100 & 29.38 & 0.03 & \(-1.226\) & 0.223 \\ \hline SR & 50 & YES & YES & 33.19 & 14.21 & 100 & 33.19 & 0.03 & \(-1.692\) & 0.129 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performance of different reconstruction algorithms for in-vivo brain image (Figure 6) (MC—motion correction, HR—upscaling using Non-Rigid Multi-Modal 3D Medical Image Registration Based Foveated Modality Independent Neighbourhood Descriptor). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compressed Sensing Quality \({}^{*}\) [\%]** & **PSNR [dB]** & **MAE** & **N** & **M** & **SD** & **t(99)** & \(p\)** \\ \hline
20 & 19.31 & 20.01 & 100 & 19.31 & 0.03 & \(-1.510\) & 0.101 \\ \hline
40 & 21.77 & 19.34 & 100 & 21.77 & 0.02 & \(-1.014\) & 0.321 \\ \hline
50 & 35.42 & 11.01 & 100 & 35.42 & 0.03 & \(-1.201\) & 0.109 \\ \hline
80 & 36.01 & 14.05 & 100 & 36.01 & 0.03 & \(-1.211\) & 0.103 \\ \hline
100 & 36.69 & 13.09 & 100 & 36.69 & 0.03 & \(-1.310\) & 0.109 \\ \hline \hline \end{tabular} \({}^{*}\)Compressed Sensing quality ratio refers to the remaining number of the input samples in comparison with fully sampled scans, i.e., 60 means that 40% of the fully sampled scan samples was removed.  
\end{table} 
Table 8: The performance of the presented algorithm for the data from the Figure 8 at different Compressed Sensing quality ratios. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reconstruction Algorithm** &
\begin{tabular}{c} **PSNR** \\ **[dB]** \\ \end{tabular} & **MAE** & **N** & **M** & **SD** & **t(99)** & \(p\)** \\ \hline LR input & 26.01 & 19.35 & 100 & 26.01 & 0.04 & 0.387 & 0.591 \\ \hline B-spline Cubic interpolation & 26.09 & 18.438 & 100 & 26.09 & 0.02 & \(-0.721\) & 0.331 \\ \hline Non-Rigid Multi-Modal 3D Medical Image Registration Based & & & & & & \\ on Foveated Modality Independent Neighborhood Descriptor & 31.01 & 14.23 & 100 & 31.01 & 0.02 & \(-1.031\) & 0.331 \\ \hline Enhanced deep residual networks for single image & 29.47 & 14.99 & 100 & 29.47 & 0.03 & \(-1.001\) & 0.266 \\ super-resolution & 29.47 & 14.99 & 100 & 29.47 & 0.03 & \(-1.001\) & 0.266 \\ \hline Image super-resolution using very deep residual chanel & 28.55 & 14.81 & 100 & 28.55 & 0.03 & \(-1.071\) & 0.219 \\ \hline Residual dense network for image super-resolution & 29.01 & 14.31 & 100 & 29.01 & 0.03 & \(-1.102\) & 0.194 \\ \hline The presented algorithm & 35.42 & 11.01 & 100 & 35.42 & 0.03 & \(-1.201\) & 0.109 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical parameters of the model performance metrics for in vivo trials, see Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compressed Sensing Quality \({}^{*}\) [\%]** & **PSNR [dB]** & **MAE** & **N** & **M** & **SD** & **t(99)** & \(p\)** \\ \hline
20 & 19.31 & 20.01 & 100 & 19.31 & 0.03 & \(-1.510\) & 0.101 \\ \hline
40 & 21.77 & 19.34 & 100 & 21.77 & 0.02 & \(-1.014\) & 0.321 \\ \hline
50 & 35.42 & 11.01 & 100 & 35.42 & 0.03 & \(-1.201\) & 0.109 \\ \hline
80 & 36.01 & 14.05 & 100 & 36.01 & 0.03 & \(-1.211\) & 0.103 \\ \hline
100 & 36.69 & 13.09 & 100 & 36.69 & 0.03 & \(-1.310\) & 0.109 \\ \hline \hline \end