# Image Interpolation Based on Spiking Neural Network Model
## 5 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Image** & **CGI** & **CED** & **PCI** & **IEDI** & **WTCGI** & **GEI** & **Proposed** \\ \hline Bike & 0.8808 & 0.8812 & 0.8803 & 0.8751 & 0.8791 & 0.8798 & **0.9071** \\ Wheel & 0.8621 & 0.8626 & 0.8668 & 0.8644 & 0.8649 & 0.8665 & **0.8986** \\ Boats & 0.8763 & 0.8801 & 0.8794 & 0.8771 & 0.8744 & 0.8796 & **0.8963** \\ Butterfly & 0.9721 & 0.9732 & 0.9720 & 0.9718 & 0.9698 & 0.9758 & **0.9992** \\ House & 0.8781 & 0.8778 & 0.8789 & 0.8783 & 0.8775 & 0.8780 & **0.8956** \\ Cameraman & 0.8711 & 0.8732 & 0.8715 & 0.8704 & 0.8692 & 0.8732 & **0.8976** \\ Baboon & 0.9125 & 0.9111 & 0.9130 & 0.9121 & 0.9112 & 0.9165 & **0.9403** \\ Peppers & 0.9032 & 0.9041 & 0.9035 & 0.9029 & 0.9026 & 0.9025 & **0.9278** \\ Fence & 0.7752 & 0.7780 & 0.7785 & 0.7763 & 0.7765 & 0.7723 & **0.7893** \\ Airplane & 0.9405 & 0.9410 & 0.9401 & 0.9389 & 0.9422 & 0.9412 & **0.9591** \\ Barbara & 0.9125 & 0.9128 & 0.9130 & 0.9114 & 0.9105 & 0.9118 & **0.9392** \\ Stars & 0.9584 & 0.9603 & 0.9617 & 0.9608 & 0.9610 & 0.9608 & **0.9762** \\ Average & 0.8952 simulates HVS instead of gradient-based techniques. The clearest advantage of SNNs is the detection of edge angles without a threshold value. The general condition for a pixel to be defined as an edge is \(\left\lvert F_{0}(i,j)-F_{g0}(i,j)\right\rvert\geq\mathrm{T}\) in the WTCGI and GEI techniques. T is a user-defined threshold value. The T value, which is generally used as a constant, is accepted as 0.01 in GEI. As seen in Figure 7, the edge values obtained with SNNs change as non-linear values, as opposed to linear ones as in a gradient. Thus, there is no need to select a threshold value. When the interpolation results seen in Figure 8 are examined visually, it is seen that the edges are preserved in a similar way to other techniques in the literature. Although the proposed method produces successful results, the computational time of the SNN model is slightly longer than those of gradient-based techniques. However, the tests show that this difference is tolerable. Figure 9 shows the average calculation times for the results obtained through MATLAB. The tests are run on a computer with an Intel Core i7 4710 HQ 2.50 GHz processor and 32 GB RAM. Although the proposed method is somewhat behind in terms of calculation times, it can be easily argued that this difference is minimal. This is quite inspiring for studies that can perform edge detection by HVS. The proposed SNN model is designed in a simpler way than existing SNN models. First of all, since the receptor field utilized is 3 \(\times\) 3 in size, the computational cost of the proposed method is approximately 1/2.5 times those in studies using 5 \(\times\) 5 dimension receptor fields [25; 28; 35]. Additionally, it does not contain additional filters such as the Gabor filter which also provides an advantage in terms of the computational cost. Another important aspect of the proposed approach is that it does not require any learning steps while detecting edges, just like gradient-based techniques. The proposed approach provides successful results by simulating HVS in a much simpler way compared to known machine learning (ML) techniques. The results obtained in this study, where SNNs are used for the first time for interpolation, will open the door to many similar studies in the future. Figure 7: Calculated difference values between a pixel and its neighbor with 0 (zero) gray level using the gradient and the proposed SNN model.