# Improvement of a Subpixel Convolutional Neural Network for a Super-Resolution Image
###### Abstract  
Super-resolution technologies are one of the tools used in image restoration, which aims to obtain high-resolution content from low-resolution images. Super-resolution technology aims to increase the quality of a low-resolution image by reconstructing it. It is a useful technology, especially in content where low-resolution images need to be enhanced. Super-resolution applications are used in areas such as face recognition, medical imaging, and satellite imaging. Deep neural network models used for single-image super-resolution are quite successful in terms of computational performance. In these models, low-resolution images are converted to high resolution using methods such as bicubic interpolation. Since the super-resolution process is performed in the high-resolution area, it adds a memory cost and computational complexity. In our proposed model, a low-resolution image is given as input to a convolutional neural network to reduce computational complexity. In this model, a subpixel convolution layer is presented that learns a series of filters to enhance low-resolution feature maps to high-resolution images. In our proposed model, convolution layers are added to the efficient subpixel convolutional neural network (ESPCN) model, and in order to prevent the lost gradient value, we transfer the feature information of the current layer from the previous layer to the next upper layer. The efficient subpixel convolutional neural network (R-ESPCN) model proposed in this paper is remodeled to reduce the time required for the real-time subpixel convolutional neural network to perform super-resolution operations on images. The results show that our method is significantly improved in accuracy and demonstrates the applicability of deep learning methods in the field of image data processing. subpixel convolutional neural network; super-resolution; residual network; image reconstruction 2025202024  
with low resolution is increased to a high-resolution area with a filter before reconstruction, which increases the computational complexity. Different methods, such as interpolation, machine learning, and deep learning, have been proposed to obtain super-resolution images to date. Super-resolution methods are divided into three main categories: interpolation methods, reconstruction-based methods, and example-learning-based methods [7]. Image resolution was first started with interpolation methods. Traditional methods, such as nearest-neighbor interpolation, bicubic interpolation, and displacement-field-based methods, are based on the correlation between new neighboring pixels created at certain intervals from existing pixel values [8]. Since these new pixels consist of uncertain values, they can create a blurry image. In the nearest-neighbor interpolation method, the value of the nearest-neighbor pixel is filled in to create a new pixel. Although it is considered the fastest calculation method among interpolation methods, the result of the image produced is rougher. In the bilinear interpolation method, the new pixel value is calculated by the weighted average of the four nearest pixel values, and a new pixel is created. A better-resolution image is obtained compared to the nearest-neighbor interpolation method. The disadvantage of this method is that the edges and corner areas of the image are smoothed. In the bicubic interpolation method, the weighted average of 16 pixels is calculated and a better-resolution image is produced. The biggest disadvantage of the bicubic method is that more calculation time is needed. It can be preferred in cases where time is not important and gives better results than other methods [9]. Although interpolation-based methods are fast, image distortions may occur due to lack of information. Deep learning is a multilayered network structure created by modeling the human brain. Deep learning has developed as a sub-branch of machine learning. Deep learning models are algorithms that establish a relationship between the data given as input and the results [10; 11].