# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 1 Introduction
The sparse representation method combines the image by using the sparseness of the image block representation in the overcomplete dictionary. [PERSON] et al. [16] proposed a remote sensing image pan-sharpening method from the perspective of compression perception as well as a remote sensing image fusion method based on local adaptive sparse representation. [PERSON] et al. [17] proposed a pan-sharpening model based on a sparse representation injection model, which uses the ARIS concept instead of compressed sensory reconstruction to create fused images through detail injection. With the development of computer technology, deep learning has been widely used in pan-sharpening. Unlike traditional methods, deep learning-based spatial-spectral fusion networks use hyperspectral images and panchromatic images as input data, allowing the network to perform image data extraction and fusion autonomously, thus avoiding errors arising from manual feature extraction. [PERSON] et al. [18] proposed a multi-scale and multi-stream fusion network that can extract spatial and spectral information at different scales. [PERSON] et al. [19] proposed a multi-scale nonlocal-attention network that focuses on enhancing multi-scale targets in the scene, thereby improving image resolution. [PERSON] et al. [20] were the first to integrate deep learning with pan-sharpening, and the SRCNN model proposed by them had decent fusion but no end-to-end mapping procedure. [PERSON] et al. [21] proposed a deep network model using a residual network structure (PanNet), which propagates spectral information directly into the reconstructed image by adding an upsampled hyperspectral image to the network output. [PERSON] et al. [22] proposed an end-to-end pan-sharpening method based on multi-scale dense networks and designed a multi-scale dense block to extract shallow features in the network. [PERSON] et al. [23] proposed a residual structure-based pan-sharpening network that learns the residuals between the input and output and improves the convergence speed of the model. The above models have achieved good fusion results, but there are still the following three problems: (i) Due to the influence of the convolutional kernel reception field, the models can only extract local spatial and spectral features, resulting in poor global connectivity of the reconstructed images, and it is difficult to adjust local features according to the global spectral spatial details of the images. (ii) The size of features in remote sensing images varies, and the existing algorithm selects a single-sized convolution kernel, which cannot achieve feature extraction for features of different sizes. (iii) The loss of feature information caused by the change in image channels in the fusion stage is not considered. To address the aforementioned issues, this paper combines the benefits of the CNN and the Swin transformer and proposes a three-stream pan-sharpening network based on a multi-scale residual dense block (MRDB) and the Swin transformer, with the goal of improving the model's ability to extract global and local features. Our contributions are as follows:  
1. A pan-sharpening network with three feature extraction branches was designed to extract and combine valuable information from the various input branches. 2. The Swin transformer feature extraction module was designed to extract global features from panchromatic and hyperspectral images. 3. Multi-scale residuals and dense blocks were added to the network to extract multi-scale local features from hyperspectral images in the image. 4. The residual fusion module was used to reconstruct the retrieved features in order to decrease feature loss during fusion.