# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 3 Proposed Method  
Recent advancements in super-resolution methods, such as RCAN [30] and SwinIR [31], have demonstrated exceptional performance on standard datasets, like DIV2K and Set14. These models utilize advanced attention mechanisms and transformer-based architectures to achieve state-of-the-art results. However, such approaches often involve complex architectures and high computational costs, which may limit their applicability in real-time scenarios, particularly in specialized domains such as medical imaging. To address these limitations, we propose the CS-SISR model, which integrates lightweight channel and spatial attention mechanisms optimized for melanoma diagnosis. This model is specifically designed to restore diagnostically relevant features, such as lesion boundaries and textures, while maintaining computational efficiency. The CS-SISR model employs the convolutional self-attention block (CSBlock) to emphasize high-frequency features. High-frequency features, such as edges and textures, significantly impact the visual quality of an image and are critical for accurate diagnosis in medical imaging. Self-attention, a method that emphasizes some values and suppresses others, effectively handles the high-frequency and low-frequency regions that influence image quality. Therefore, we introduce the CSBlock structure, which combines channel and spatial attention to emphasize high-frequency features effectively. The CSBlock structure employs a hierarchical feature extraction process that integrates both shallow feature extraction and deep feature extraction to enhance the reconstruction quality of images. The  
Figure 6: Spatial attention module. architecture of the proposed method, as shown in Figure 7, illustrates the flow of feature extraction and upsampling. Shallow feature extraction focuses on capturing low-level features such as edges, textures, and basic patterns in the initial layers of the network. These features are extracted using convolution operations, which process the input data to highlight essential visual elements. To ensure that the shallow features contribute effectively to the final reconstruction, skip connections are employed. These connections directly link the output of the shallow layers to the deeper layers, preserving critical base-level information necessary for accurate image reconstruction. Deep feature extraction involves learning complex and abstract features in the later stages of the network. This process focuses on capturing high-level patterns and relationships that are essential for reconstructing detailed and high-quality images. To enhance feature representation, skip connections and concatenation are utilized. Skip connections ensure that shallow, low-level features are retained and seamlessly integrated with deeper, high-dimensional features. By combining these features, the network effectively preserves low-level details while learning high-level abstractions, resulting in an improved and comprehensive feature representation. The CSBlock structure combines shallow and deep features through skip connection and concatenation. Skip connection directly connects the output of a previous layer to a current layer within the network. This design helps mitigate the problem of gradient vanishing, which often occurs in deep networks, by ensuring that gradients can flow uninterrupted through the layers during backpropagation. By maintaining this direct connection, the network is able to learn deeper and more complex features effectively, contributing to an enhanced image reconstruction quality. Concatenation combines feature maps from different depths of the network, enabling the extraction of a wider range of features. This approach allows the model to integrate both low-level and high-level features, creating a richer and more diverse feature representation. By leveraging features at varying depths, concatenation enhances the network's ability to capture fine details and complex patterns, improving the overall performance of the super-resolution method.