# Multi-Scale Cross-Attention Fusion Network Based on Image Super-Resolution
## 1 Introduction  
Image super-resolution (SR) is a fundamental task in computer vision, the primary goal of which is to reconstruct a low-resolution image (LR) into a high-resolution photo (HR). Image super-resolution (ISR) reconstruction is an ill-posed problem because multiple HR images may degrade into the same LR image, and details may be lost in the degradation process. Image super-resolution has been widely studied and applied to medical images, remote sensing images, video surveillance, and other fields needing high-frequency information. In recent years, as deep learning technology has made significant progress in computer vision, this technology has been applied to more tasks. Compared with the image super-resolution methods based on interpolation [1], reconstruction [2], and learning [3, 4], the use of deep learning methods can reconstruct high-frequency information more effectively.  
SRCNN [5] first applied a Convolutional Neural Network (CNN) to the field of image super-resolution and solved the problem through the mapping function from LR input to HR output. Since then, deep CNN-based methods have been widely used in ISR. Following SRCNN, methods such as FSRCNN [6], ESPCN [7], VDSR [8], EDSR [9], LapSRN [10],and DRRN [11] provide a wider sensory field by deepening the network structure and introducing a residual learning mechanism to alleviate the gradient vanishing problem that increases with network deepening.  
Recently, CNN-based methods, such as MSRN [12], MSFRNE [13], and MSAR [14], have demonstrated the ability to further enhance network performance by making full use of multi-scale extracted feature information to increase image texture details. However, despite advances in these methods, more work still needs to be done on the effective fusion of multi-scale features and deep utilization of attention mechanisms. In particular, how to fully use different multi-scale information and enhance the ability of feature information expression while maintaining network efficiency. Therefore, to solve these problems, this paper proposes a multi-scale cross-attention fusion network (MCFN) for the image super-resolution task. The main contributions of this paper are as follows:  
1. A multi-scale cross-attention fusion network (MCFN) is proposed to achieve total extraction and compelling fusion of feature information at different scales and promote high-quality image reconstruction.
2. A multi-scale Trans-attention module (MTM) is proposed to efficiently extract and fuse multi-scale feature information. MTM utilizes a pyramid multi-scale module (PMM) to extract feature information of various scales, which is then input into a Cross Attention Fusion module (CFM) in a cross-module manner. This approach incorporates a cross-connect strategy that combines channel and spatial attention mechanisms to fuse the multi-scale feature information effectively and capture the correlation dependence between them.
3. An improved integrated Attention Enhancement module (IAEM) is proposed to extract more feature information from the middle layer through a dense connection strategy. The module learns the correlation between the middle layers and integrates the feature information of each module effectively.
4. The objective metrics and subjective vision of public datasets show that our method is competitive compared with existing methods. At the same time, we prove the proposed method's effectiveness through many ablation and experimental studies.  
This paper is organized as follows: Section 2 will introduce the relevant studies. Section 3 will elaborate on our proposed method and structure. Section 4 will show the experimental results of the method on a public benchmark dataset. The last section will summarize the main conclusions of the paper.