# Improvement of a Subpixel Convolutional Neural Network for a Super-Resolution Image
## 2 Materials and Methods
### R-ESPCN Structure
Similarly, the SSIM values in the images increased by 0.89%, 0.08%, 0.04%, 2.54%, and 0.09%, respectively. When the PSNR values were examined, it was found that there was an average increase of 1.28 dB, and the average increase rate in SSIM values was 0.72%. When looking at the set14 data in Figure 9, it is seen that the R-ESPCN model we proposed gives more successful results than the ESPCN model, and the PSNR and SSIM values are higher. According to the set14 data in Figure 9, the PSNR values in the images increased by 1.56 dB, 0.49 dB, 1.54 dB, 1.52 dB, 1.26 dB, and 1.27 dB, respectively. Similarly, the SSIM values in the images increased by 0.4%, 0.39%, 0.21%, 0.06%, 1.03%, and 0. In this article, we tested not only on classical datasets but also on medical images with the dicom extension. First, we converted dicom extension images to known image formats, such as jpeg and png. According to the medical image data with the dicom extension in Figure 11, the PSNR values in the images increased by 1.58 dB, 4.55 dB, 6.70 dB, 12.56 dB 10.74 dB, and 7.17 dB, respectively. Similarly, the SSIM values in the images increased by 0.64%, 26.6%, 13.05%, 44.29%, 28.95%, and 25.57%, respectively. According to the test results for medical images, it is seen that the R-ESPCN model gives significantly better results than the ESPCN model, and the PSNR and SSIM values are higher. When the PSNR values were examined, it was found that there was an average increase of 7.22 dB, and the average increase rate in SSIM values was 23.18%. Self-supervised learning systems that learn the natural structure of image data through prediction can be developed to facilitate experts in making clinical diagnoses on medical images without the need for extensive labeled data [29]. Figure 6.1 Comparison of ESPCN and R-ESPCN models on the set14 dataset: baboon, comic, Barbara, pepper, zebra, and flowers. In Figure 12, the PSNR values in the images belonging to the article authors [PERSON] and [PERSON] increased by 5.28 and 0.92 dB, respectively. Similarly, the SSIM values in the images increased by 3.78% and 0.14%, respectively. When the PSNR values were examined, it was found that there was an average increase of 3.1 dB, and the average increase rate in SSIM values was 1.96%. The experimental test results show that our proposed model gives significantly better PSNR and SSIM values. The improvements made in the image resolution in the model we proposed can provide a new perspective on real-world applications, such as the diagnosis of security cameras, medical images, face recognition, object detection, remote sensing, and satellite imaging. Figure 6.2 Comparison of ESPCN and R-ESPCN models on the security camera image dataset. ## 4 Conclusions

It is used widely for image reconstruction to obtain higher-resolution images in the fields of face recognition, medical imaging, security cameras, and satellite imaging. Our

Figure 6.3 Comparison of ESPCN and R-ESPCN models on the medical image dataset with the dicom extension. Figure 6.4 Comparison of ESPCN and R-ESPCN models on images belonging to article authors. proposed R-ESPCN model overcomes gradient loss, computational complexity, and other problems that occur in deep learning networks and obtains super-resolution images. By giving low-resolution images directly as input to the network, computational complexity is prevented. A two-layer convolutional network is used to extract more features from the images. The residual network is used to prevent the vanishing gradient problem in the network. Our proposed model reduces the computation amount by directly performing operations at the subpixel level compared to classical image super-resolution methods based on deep learning. Compared to the ESPCN method, it provides a more robust representation of the image using the deep residual network method and offers new ideas to shorten the depth of the network. With the subpixel convolution layer, the correlation of the feature information of the image is preserved, and the construction time is reduced.