# Image Interpolation Based on Spiking Neural Network Model
## 1 Introduction  
Image interpolation is still used today to improve image quality in many fields of image processing (such as medical sciences, natural sciences, or satellite images), and new techniques continue to be developed [1; 2; 3]. Generally, interpolation techniques are examined in two groups: super-resolution techniques [4; 5; 6; 7] and sample-free techniques [8; 9; 10; 11; 12; 13; 14]. Super-resolution-based approaches require a training phase based on learning the relationships between low- and high-resolution samples of many images. On the other hand, sample-free methods perform the interpolation process through mathematical formulae without any training steps. Therefore, their most important advantage is that they are fast. The most known sample-free approaches are the nearest, bilinear, and bicubic interpolation methods [15]. Although the most successful results are generally obtained with bicubic interpolation, the most significant disadvantage of this method is edge loss [16]. Recently, many interpolation techniques based on the detection of edge pixels have been developed [8; 9; 10; 11; 12; 13; 14]. The general purpose of these studies is to reduce edge loss and increase image quality by performing different interpolation operations on edge pixels and non-edge pixels. The CGI method [9], proposed in 2013, is one of the first and most well-known techniques to detect edge and non-edge pixels in the interpolation process. The CGI method performs interpolation using a 1D cubic filter for edge pixels and a 2D bicubic filter for non-edge pixels. In 2016, with the development of the CGI method, the CED [10] interpolation technique was proposed. The CED technique also uses different filters according to the edge status of the pixels. A similar approach, the PCI [11] interpolation technique, uses the Canny edge detector in the edge detection phase. The IEDI [8] technique applies different interpolation approaches on the edge and non-edge pixels with the help of the Canny edge detector. On the other hand, the WTCGI [12] technique includes very similar steps to the CGI technique and uses wavelet transform for edge detection. In one of the latest studies, the GEI [14] technique uses gradient-based edge detection, marks the pixels as edge and non-edge, and then applies different interpolation approaches to these pixels. The common feature of edge-based interpolation studies in the literature is that they have used edge detection approaches, such as gradients or wavelets, in which the gray-leveldifferences of neighboring pixels are calculated. Gradient approaches, which are based on the linear difference between the gray-level values of the pixels, have been used for many years for edge detection in many different areas due to their easy calculations [17; 18]. The gradient is usually calculated with the help of small-size filters (e.g., 2 \(\times\) 2 or 3 \(\times\) 3) [15]. Moreover, the most important disadvantage of the gradient is that it has high noise sensitivity and detects false edges [19]. However, the way the human visual system (HVS) detects edges is quite different from the way gradient-based techniques detect edges. The detection of the gray-level change between pixels of a ganglion cell in HVS [20] is shown in Figure 1. Even if the change in the gradient value is linear, the ganglion cells detect the change at only two points. Therefore, it is clear that different approaches should be used instead of gradients to develop an interpolation technique compatible with HVS. Especially in the last decade, edge detection techniques inspired by HVS have achieved significant success. In the 2000s, bioinspired edge detection techniques that simulate HVS have begun to be developed as an alternative to gradient-based linear edge detection techniques. In one of the first image processing studies on HVS, a double-layer network design for edge detection was developed in 1993 [21]. It is seen that this double-layer network structure has been used in almost all HVS-based studies carried out to date.