# A Novel Implicit Neural Representation for Volume Data
## 4 Results and Discussion
### Comparison with Existing Methods  
In this section, we compare the performance of SIREN with our architecture and SIREN without our architecture which is used in the Ref. [2] with 2, 3 and 4 layers and 128 neurons for each layer. From the general perspective, both methods use SIREN to compress volumetric medical images. In the Ref. [2] they applied SIREN without any downsampling and upsampling techniques which led to high GPU memory consumption and low training speed. In Figure 13 there are three bar charts, showing that SIREN with our architecture outperforms SIREN without our architecture in the Ref. [2] in terms of quality, speed, and GPU memory consumption with 2, 3, and 4 MLP layers. As can be seen, there are considerable gaps between our technique and SIREN in the Ref. [2] in terms of training speed and GPU memory consumption. The PSNR gap is decreased by increasing the number of layers, but our architecture still outperforms the SIREN in the Ref. [2]. Table 6 compares the results of using SIREN to compress volumetric medical images in the Ref. [2] with using SIREN with our architecture in three factors of PSNR, training speed and GPU memory consumption. Figure 14 compares the final results of using SIREN in the Ref. [2] and using SIREN with our architecture with 2, 3 and 4 layers. Table 7 shows the comparison of the results of [PERSON]'s [52], [PERSON]'s [53], SIREN [2] and ours in terms of PSNR.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Number of Layers** & **SIREN without Our Architecture [2]** & **SIREN with Our Architecture** \\ \hline \multirow{3}{*}{Best PSNR (dB)} & 2 layers & 25.269 & **34.670** \\  & 3 layers & 28.800 & **34.865** \\  & 4 layers & 30.689 & **35.140** \\ \hline \multirow{3}{*}{Training time(s) (for 15000 iters)} & 2 layers & 664.92 & **55.76** \\  & 3 layers & 1004.21 & **74.80** \\  & 4 layers & 1336.53 & **99.61** \\ \hline \multirow{3}{*}{GPU memory consumption (KB)} & 2 layers & 10,254 & **1,038** \\  & 3 layers & 10,512 & **1,296** \\ \cline{1-1}  & 4 layers & 10,770 & **1,554** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Display and comparison of the final results that were obtained using SIREN in the Ref. [2] and using SIREN with our architecture. As it can be seen, using SIREN with our architecture outperforms using SIREN in the Ref. [2] in three terms of quality, speed, and GPU memory allocation with 2, 3 and 4 MLP layers.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Methods**} & **Best PSNR** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Shen’s [52] \\ Mishra’s [53] \\ SIREN [2] (2 layers) \\ SIREN [2] (3 layers) \\ SIREN [2] (4 layers) \\ Ours (2 layers) \\ Ours (3 layers) \\ Ours (4 layers) \\ \end{tabular} } & 27.50 \\  & 29.33 \\  & 25.269 \\  & 28.800 \\  & 30.689 \\  & **34.670** \\ Ours (3 layers) & **34.865** \\ Ours (4 layers) & **35.140** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of results of our technique with SIREN [2], Mishra’s [53] and Shen’s [52] in terms of PSNR.  
Figure 13: Bar charts compare the results of using SIREN without our architecture [2] and with our architecture. As can be seen, SIREN implementation with our architecture outperforms using SIREN without our architecture [2] in terms of quality, speed, and GPU memory allocation with 2, 3, and 4 MLP layers.  
Figure 14: Comparison of the quality of reconstructed HR using SIREN in the Ref. [2] and using SIREN with our architecture with 2, 3 and 4 MLP layers.