# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 1 Introduction
### Deep Learning-Based Methods  
High-resolution image generation is essential in computer vision, with the aim of converting low-resolution images into high-quality, high-resolution images. Previous research has attempted to enhance image resolution using traditional interpolation and upscaling techniques. However, these methods often do not adequately recover detailed image information or amplify high-frequency noise. In contrast, deep learning can effectively extract and restore high-frequency and fine-grained information from images through neural network training based on large-scale datasets [11]. Various approaches--such as machine learning, prediction-, reconstruction-, and boundary-based methods--have been explored. In addition, hybrid techniques that combine deep learning with models and signal processing have also emerged. These studies aimed to improve the accuracy, color fidelity, and quality of images generated. Overcoming the limitations of the existing methods for high-resolution image generation provides a novel approach to generating high-quality images with realistic and sharp details. High-resolution images are applicable in various fields, including high-quality video production, medical image analysis, and security, emphasizing their importance. Among these approaches, very deep super-resolution (VDSR) [12], enhanced deep super-resolution (EDSR) [13], and bicubic interpolation have garnered attention for generating high-resolution images. VDSR employs a deep neural network architecture to generate high-resolution images by learning the residuals between the low- and high-resolution images. This allows the extraction of high-frequency details from low-resolution input images to generate high-resolution images. However, using a deep network in VDSR increases the risk of overfitting and often requires voluminous training data. Additionally, the training and inference processes of VDSR can be computationally intensive and time-consuming. Similarly to VDSR, EDSR is a deep learning-based method for generating high-resolution images that uses a deep and robust neural network. However, EDSR further improves the structure of the residual blocks and employs a larger receptive field to generate more accurate and natural high-resolution images. This enables the extraction of more high-frequency details. However, the increased complexity of the EDSR structure can lead to many parameters, high resource requirements, and challenges such as gradient fading. Bicubic interpolation is a traditional method that is used to generate high-resolution images [14]. It is used to enlarge low-resolution input images into high-resolution images by interpolating high-resolution values using neighboring pixel values. However, bicubic interpolation may not accurately reproduce high-frequency details during image generation, resulting in overly smooth images. Moreover, as an interpolation technique used in upsampling, it may not accurately model the complexity and diversity of real images. These deep learning-based methods can be challenging for diagnosing and analyzing melanoma. Therefore, this study proposes a channel-spatial block-based super-resolution (CS-SISR) method, an artificial intelligence technique for melanoma diagnosis and high-resolution image generation. CS-SISR is a deep learning-based approach that converts low-resolution images into high-resolution images. CS-SISR addresses the limitations of high-frequency regions and feature extraction using a convolutional self-attention (CS) block that combines channel and spatial attention. The core components and their roles in the CS-SISR method are summarized in Table 1 below. Channel attention learns the importance of each channel in the input feature map to emphasize high-frequency features. Spatial attention learns the importance of each spatial position in the input feature map to enhance the spatial information of the high-frequency features.