## 1 Introduction
Research on predicting the detailed components of HR images from single LR images has been extended to the field of single-image super-resolution (SISR) based on deep learning [19; 20]. [PERSON] et al. introduced a super-resolution convolutional neural network (SRCNN) [21]. The convolutional neural network states that each nonlinear transformation using a data-driven filter corresponds to traditional patch extraction, nonlinear mapping (from LR to HR patches), and reconstruction. The SRCNN is a combination of upsampling methods, a model framework, a network design, and a learning strategy and serves as a cornerstone of early SISR research. [PERSON] et al. proposed the so-called very deep super-resolution (VDSR) convolutional network, inspired by VGG-net [22]. Using 20 layers and many small cascading filters, they were able to utilize the contextual information throughout the image. Adjustable gradient clipping was used to compensate for slow convergence. Residual blocks have been actively used since VDSR was proposed. SISR based on a generative adversarial network (GAN) was also introduced [23]. [PERSON] et al. demonstrated SRResNet (SRGAN), which is used for the perceptual loss function and consists of adversarial loss and content loss to improve the human visual system perspective. In addition, deep learning models that implement SISR in various ways, such as recursive, densely connected, and attention-based networks, have been introduced [24]. Because most of the image resolution is in the high-frequency range, it is also of interest to extract the edge image separately to improve image sharpness. [PERSON] et al. presented a multiscale deep encoder-decoder-based SISR method with phase congruency (MSDEPC) [25]. They proposed a phase congruency edge map to maintain the structural edge features of an image according to the subsampling of different scales. It has been demonstrated that this method can appropriately integrate edge details better than existing deep learning models. Ultrasound imaging is highly dependent on image resolution when observing the shape and blood flow angles, which is one of the reasons why SISR is needed [26]. In this study, we investigated a deep learning-based SISR process to improve sonographic image quality using the MSDEPC model. The purpose of the paper is to identify how much the MSDEPC model, which performs SISR by extracting an accurate edge structure, improves image quality in ultrasound images according to important clinical imaging sites and to reveal its usefulness. The clinical validation of the deep learning model will provide useful information and further research directions for other researchers. We evaluated the full width at half maximum (FWHM), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) [27; 28]. In the following sections, we briefly describe the implementation of the simulation and experiment and discuss the results in detail. The remainder of this manuscript is divided into three sections: Section 2 discusses the MSDEPC model architecture for SISR in ultrasound imaging, the datasets used for model training and testing, and the quantitative evaluation factors used. Section 3 describes the results and discussion regarding the Field II simulation and experiment of clinical images pertaining to the carotid artery, a liver hemangioma, breast malignancy, a thyroid carcinoma, and obstetric nuchal translucency. Finally, Section 4 presents the conclusions that can be drawn from our study.