# Improved MSRN-Based Attention Block for Mask Alignment Mark Detection in Photolithography
## 2 Related Work
### Deep Learning for Super-Resolution  
Reconstruction from a single low-resolution image to a high-resolution image has traditionally been an important technique in the field of computer vision. Image Super-Resolution (SR) converts a low-resolution image into a high-resolution image. At high resolution, deep learning-based models have recently achieved many advantages and improvements over conventional methods. In general, the problem of reconstructing a high-resolution image from a low-resolution image is defined as an improperly established, ill-posed inverse problem. Local patch-based SR techniques using polynomial-based interpolation and linear mapping such as bicubic interpolation have been widely researched. The SR algorithm uses linear mapping to generate high-quality, high-resolution images with relative complexity and computational amounts. LR and HR learn linear mapping from a dictionary to generate high-resolution patches. Anchored Neighborhood Regression (ANR) and adjusted ANR (A+) have shown relatively high performance [26; 27]. However, implementing a complex and nonlinear high-resolution model using these techniques is difficult. Deep learning-based SR algorithms are being studied through CNNs and are showing high performance compared with existing algorithms. They use a multi-layer network stacked into layers to learn convolutional filter parameters, which can be accurately transformed by precisely analyzing the complex nonlinear relationships between low-resolution inputs and high-resolution outputs. Dong et al. [28] proposed the Super-Resolution Convolutional Neural Network (SRCNN), a network that introduces deep learning to the super-resolution problem. SRCNN applied an end-to-end method, and all steps were processed in one integrated network. As the input LR image, an upscaled image is used by applying the bicubic interpolation method. There was no difference in the resolution of the images as they passed through the CNN network. This study showed high performance by solving the SR problem from the points of view of conventional signal processing and deep learning. It includes a network with a three-layer Fully Convolution Network structure and an algorithm to improve the image quality of enlarged images by bicubic interpolation. Figure 3 shows the SRCNN/FSRCNN structure. The network showed higher performance compared to the existing SR algorithm and proved that a deep learning-based network is possible even with the SR algorithm. Dong et al. [28] then proposed a Fast Super Resolution Convolutional Neural Network that increases the model performance while reducing the amount of computation and the number of filter parameters to reduce the weight of the network. Adding a deconvolution layer enlarges the image in the network, and training generates a more optimized high-resolution image. The deconvolution operation serves to increase the horizontal and vertical sizes of the feature map and can increase the output image with resolution. If such a low-resolution input LR image is used, the amount of computation is reduced in proportion to the square of the multiple to be upscaled. As a result, FSRCNN has the advantage of showing a lower number of parameters and higher computational speed compared to SRCNN and higher performance. Using a deep learning model to design a deep network increases the amount of computation and the number of parameters. However, when learning a deep network, gradient-vanishing or gradient-exploding problems may occur in the backpropagation process toward the input layer, and filter parameters may not be properly learned. Therefore, even if the network is designed deeply, the performance of the network does not increase proportionally. To solve this problem, He et al. [29] proposed ResNet, which is a network that can improve performance without problems, even in deep networks. Residual learning is a method of adding the input LR image to the final output HR image and learning the difference value between the two images.