# A Study on the Super Resolution Combining Spatial Attention and Channel Attention
###### Abstract  
Existing CNN-based super resolution methods have low emphasis on high-frequency features, resulting in poor performance for contours and textures. To solve this problem, this paper proposes single image super resolution using an attention mechanism that emphasizes high-frequency features and a feature extraction process with different depths. In order to emphasize the high-frequency features of the channel and space, it is composed of CSBlock that combines channel attention and spatial attention. Attention block using 10 CSBlocks was used for high-frequency feature extraction. In order to extract various features with different degrees of feature emphasis from insufficient low-resolution features, features were extracted from structures connected with different numbers of attention blocks. The extracted features were expanded through sub-pixel convolution to create super resolution images, and learning was performed through \(L_{1}\) loss. Compared to the existing deep learning method, it showed improved results in several high-frequency features such as small object outlines and line patterns. In PSNR and SSIM, it showed about 11% to 26% improvement over the existing Bicubic interpolation and about 1 to 2% improvement over VDSR and EDSR.  
super resolution; channel attention; spatial attention; parallel structure +
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pixel. This minimizes block-shaped artifacts and shows a more natural result than nearest neighbor interpolation. However, the same calculation is performed on high-frequency components such as outlines and textures, resulting in blurry texture expressions. On the other hand, the deep learning-based method can accurately generate empty pixel values through more complex and diverse operations than the existing interpolation-based method. Deep learning-based methods generate new pixel values through CNN, and include SRCNN [6], VDSR [7], and EDSR [8]. SRCNN is a model that proposes end-to-end learning based on deep learning. It uses bicubic interpolation-extended images as input and consists of three convolution layers. On the other hand, VDSR is a deep network model and consists of 20 convolution layers deeper than the three layers of SRCNN. Effective learning is possible through residual learning that adds input images and output images. EDSR is a 69-layer network structure that is deeper than VDSR, and efficient learning was conducted using residual blocks [9]. Existing deep learning-based methods create a network without considering high-frequency components such as outlines and textures, which makes it difficult to create clear images. Recently, an attention-based super resolution method using such high-frequency feature emphasis has been proposed [10; 11].  
In this paper, SISR using attention is proposed to solve the high-frequency region and feature extraction problems that were not considered in the existing CNN-based super resolution method. In order to emphasize high-frequency features, feature enhancement for high-frequency features was carried out with CSBlock, which combines channel attention and spatial attention, and various features were utilized through feature extraction processes of various depths. In addition, the deep network structure helps improve performance, but there is a gradient vanishing/exploding problem, which is a difficult learning problem. The feature map extracted in this way was extended using sub-pixel convolution, and an HR image was created through residual block and convolution. In order to learn this structure, it was composed of a 48 \(\times\) 48 size LR patch and an HR patch, and learning was conducted using \(L_{1}\) Loss. Through this structure, natural HR images were created by emphasizing high-frequency features that greatly affect image quality and utilizing various features.