# Image Interpolation Based on Spiking Neural Network Model
## 5 Experimental Results  
The proposed method is tested on the 12 most commonly used images for testing interpolation techniques. The results are obtained with the CGI [9], CED [10], PCI [11], and IEDI [8] techniques for all images. Apart from these, the results of the recently developed WTCGI [12] and GEI [14] techniques are also included in the comparisons. First, all images are originally downsampled to a size of \(\frac{1}{2}\times\frac{1}{2}\). Then, by upsampling to \(2\times 2\) with the tested methods, the obtained images are ensured to be the same size as the original image. The upsampled images are compared to the original images, and the results are obtained as PSNR and SSIM. The proposed SNN model is tested in MATLAB with the following parameters: \(c_{m}=1\) uF/mm\({}^{2}\), \(E_{l}=-44.42\) mV, \(g_{l}=0.003\) uS/mm\({}^{2}\), \(\tau_{\alpha x}=4\) ms, \(\tau_{ih}=10\) ms, \(\tau_{nf}=0\) ms, \(E_{ex}=36.78\) mV, \(E_{ih}=-72.14\) mV, \(E_{\text{out}}=36.78\) mV, \(v_{\text{reset}}=-70\) mV, \(\gamma_{th}=-55\) mV,\(A_{ex}=0.0141\) mm\({}^{2}\), \(A_{ih}=0.0281\) mm\({}^{2}\), \(T=100\) ms, and \(\mathrm{d}t=0.1\) ms. The weight matrices of the synapses for m\({}_{A1}\) and m\({}_{A2}\) are as follows:  
\[w_{ex}= \begin{bmatrix}0&0&0\\ 0.32&0.36&0.32\\ 0&0&0\end{bmatrix} ,\ w_{ih}= \begin{bmatrix}0.16&0.18&0.16\\ 0&0&0\\ 0.16&0.18&0.16\end{bmatrix} \]  
Table 1 shows the PSNR results of the techniques that are compared in this study. The best results are marked in bold. According to the PSNR results, the proposed method is more successful both in individual images and on average. When compared with the relatively new and successful PCI and GEI techniques, it is seen that using the edges detected by the proposed SNN model increases the success of interpolation. Another metric used to evaluate the performance of the proposed method is SSIM. According to the SSIM results in Table 2, the proposed method is more successful than the other techniques under investigation. The proposed method has significant success versus both PCI and GEI techniques for each of the 12 images tested in terms of SSIM metric. The success of the proposed method according to the SSIM metric [43], which is known to correlate well with human visual perception, is another indicator of the usefulness of SNN-based approaches modeling HVS. The most important difference that distinguishes the proposed method from state-of-the-art techniques is that the edge pixels are determined with a new SNN model that \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Image** & **CGI** & **CED** & **PCI** & **IEDI** & **WTCGI** & **GEI** & **Proposed** \\ \hline Bike & 25.82 & 25.82 & 25.90 & 25.17 & 25.21 & 25.85 & **26.60** \\ Wheel & 21.01 & 20.98 & 21.22 & 20.31 & 20.57 & 21.32 & **21.53** \\ Boats & 29.51 & 29.56 & 29.77 & 29.24 & 29.32 & 29.42 & **29.71** \\ Butterfly & 29.27 & 29.24 & 29.31 & 28.97 & 28.97 & 29.26 & **29.55** \\ House & 32.83 & 32.71 & 32.88 & 32.31 & 31.87 & 32.84 & **33.17** \\ Cameraman & 25.86 & 25.9 & 25.81 & 25.48 & 25.76 & 25.83 & **26.09** \\ Baboon & 22.50 & 22.41 & 22.53 & 22.41 & 22.35 & 22.59 & **22.82** \\ Peppers & 30.88 & 30.77 & 30.87 & 30.47 & 30.19 & 30.81 & **31.12** \\ Fence & 25.70 & 25.63 & 25.84 & 25.61 & 25.69 & 25.75 & **26.01** \\ Airplane & 26.54 & 26.49 & 26.59 & 26.6 & 26.10 & 26.61 & **26.88** \\ Barbara & 23.75 & 23.64 & 23.82 & 23.54 & 23.41 & 24.01 & **24.25** \\ Stars & 34.13 & 33.94 & 34.38 & 33.36 & 33.71 & 34.33 & **34.67** \\ Average & 27.32 & 27.26 & 27.41 & 26.96 & 26.93 & 27.39 & **27.70** \\ \hline \end{tabular}
\end{table} 
Table 1: PSNR comparison results of interpolation techniques.