# Improvement of a Subpixel Convolutional Neural Network for a Super-Resolution Image
###### Abstract
Thanks to methods based on deep neural networks, the accuracy and computational performance of a low-resolution structured image are quite high compared to those of images created by traditional methods. Methods based on deep neural networks can create successful image resolutions compared to traditional methods, such as machine learning, bilinear interpolation, nearest-neighbor interpolation, and bicubic interpolation. When deep learning is compared to machine learning, generalization increases significantly since it reduces the number of parameters used in the training of the network. The process deep neural network models learning nonlinear relationships is much better compared to traditional models, and the image construction accuracy is quite high [12]. As researchers' interest in deep learning methods increased, these started to be used to increase image resolution. Researchers working on this subject have achieved good results by using convolutional neural networks to increase image resolution. Papers that refer to image quality improvement using deep learning approaches, similar to the super-resolution techniques discussed in this article, have attracted considerable interest [13]. According to the current information, [PERSON] and colleagues, who made the first applications of convolutional neural networks for single-image super-resolution, achieved good results by modeling a super-resolution convolutional neural network (SRCNN) [14]. A comparison image of the SRCNN model with bicubic interpolation in the published article on the SRCNN model is given in Figure 1.  
Researcher [PERSON] and colleagues showed in 2016 that the concept of generative adversarial networks (GANs), like other convolutional neural networks, can overcome the problems encountered in image processing and that the system can be useful [15]. The aim of a GAN is to produce new images by fitting results close to the real values. In a later process, the SRGAN model was developed, and the quality of the produced images was increased. The success of deep learning methods in super-resolution image reconstruction enables the emergence of different neural networks every day. There are many methods for super-resolution applications. Many methods assume that multiple images of the same scene with different perspectives and unique prior transformations are available as low-resolution samples. Enhanced deep super-resolution (EDSR), efficient subpixel convolution neural networks (ESPCNs), fast super-resolution convolution neural networks (FSRCNN), and Laplacian pyramid super-resolution networks (LapSRNs) are examples of super-resolution methods. EDSR method was developed after SRResNet. Although super-resolution processing time and memory consumption problems are solved in the SRResNet model, it is not an ideal model for the super-resolution method since it uses ResNet in image classification. The EDSR model, which shows good performance, was developed by removing unnecessary layers in the ResNet model. In the SRResNet model, the Batch Normalization (BN) layer, which normalizes features, was removed from the architecture because it loses range flexibility and uses much less memory during training [16]. Figure 2 shows comparatively how the residual networks of both models are used. Figure 1: Comparison of SRCNN and bicubic interpolation [14]. Figure 2: Comparison of the residual blocks in SRResNet and EDSR [16]. The deep SRCNN with a three-layer structure was applied at high resolution based on nonlinear mapping image reconstruction [14]. Due to factors such as blurring in the image and not being at the desired level of real time in the SRCNN, an improvement was made and the FSRCNN structure was developed as a faster model [8]. The deconvolution process was introduced in this model, and more mapping layers were added with small-size filters. Although the SRCNN structure performs convolution on low-resolution images, due to its high computational complexity, the efficient subpixel convolutional neural network (ESPCN [17]) was proposed [18].