## 3 Results and Discussion
Because it is difficult to obtain LR and HR paired images in the real world, we generated LR images through upsampling interpolation (e.g., bicubic interpolation) after downsampling the HR images. However, LR images obtained in the real world have additional blurring (e.g., motion artifacts) and noise components compared to virtually generated LR images. Therefore, when a network trained on virtual LR and HR paired images is applied to an actual LR image, its quality may be poor. Recently, results regarding the generation of deteriorated LR images using a GAN and using them as training data have also been reported [49]. These efforts must be discussed in terms of medical images, which have many restrictions. Second, an appropriate normalization method was discussed. BN, commonly used in deep learning, is a technique that helps students learn by normalizing the distribution of intermediate layers. However, it has also been claimed that there is a disadvantage in removing the flexibility of features in SISR; therefore, various discussions are underway [50]. In this study, the MSDEPC model used BN; however, it is expected that such claims will need to be verified using ultrasound images in the future. Third, the upsampling size was determined to be appropriate. We can determine the size of the downsampling mathematically in the HR images, and the existing methods argue that the images are similar to the HR images. However, when an HR image is restored, it may be unclear whether the detailed information of the actual image has been fully restored when the LR image has an extremely low resolution [51]. In particular, the loss of detailed information can lead to a decrease in diagnostic accuracy in the case of medical imaging. Finally, the PC detects an edge in the frequency domain. In general, gradient-based edge operators (e.g., Sobel, Canny, etc.) are vulnerable to brightness and contrast changes because they extract edges from the spatial domain, but PC has the advantage of being uncomfortable with brightness and contrast changes because they extract edges from the frequency domain. However, it is vulnerable to noise components [52]. Noise interferes with feature extraction and becomes an obstacle to extracting an accurate edge map. A method to remove speckle noise from ultrasound images while maintaining features as much as possible has been introduced, but a careful approach is required [53]. Therefore, this issue is a very important point of discussion. Research on the application of three-dimensional (3D) printing technology in diagnostic medical ultrasound imaging is being actively conducted by many researchers. Three-dimensional printing technology will be helpful in the deep-learning-based SISR process when using the MSDEPC model proposed in this study. [PERSON] et al. conducted a study on 3D printing technology for structures based on acoustic cavitation directly generated by focused ultrasound, making the precise modeling of human organs possible [54]. [PERSON] et al. modeled the left ventricle of the heart and analyzed the applicability of wavelet-thresholding image-processing technology after acquiring ultrasound images [55]. We expect that the applicability of phantoms using 3D printing, which has been proven in various studies, to ultrasound images will help build various datasets in situations where it is difficult to secure deep learning datasets.