# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 1 Introduction  
Recent years have seen specific advancements in the field of remote image processing. [PERSON] and [PERSON]'s (2020) [1] and [PERSON]'s (2022) [2] work contains the original theories for this purpose (2022). They also presented a brand-new theory of quantum image processing. In this view, with the development of imaging systems and remote sensing technologies, the types of sensors used in remote sensing have become diverse, allowing for the acquisition of an increasing number of types of data, most commonly hyperspectral and panchromatic images [3]. Panchromatic images have high resolution but lack spectral information, which is not conducive to the classification of ground cover. Hyperspectral images contain both spatial and spectral information, making them useful for various applications, including environmental monitoring and image classification. However, obtaining hyperspectral images with high spatial resolution is challenging due to hardware limitations. Currently, most sensors can only capture high-resolution panchromatic (PAN) images and low-resolution hyperspectral (LRHS) images. In order to make full use of the multi-source information from remote sensing images and to obtain multi-spectral images with high spatial resolution, the pan-sharpening technique was developed. Pan-sharpening fuses hyperspectral and panchromatic image data from the same scene to produce a new image with a higher spatial resolution than hyperspectral images [4]. It can help researchers acquire higher-quality hyperspectral data, thus enhancing the effectiveness of practical applications. Traditional pan-sharpening methods fall into three main categories: component replacement, multi-scale decomposition, and sparse representation. The component replacement method is the most primitive pan-sharpening method and is generally implemented in three steps as follows: (i) first, transfer the hyperspectral image into another space to obtain the various components of the hyperspectral image;(ii) then, replace one of the components with a panchromatic image; and (iii) finally, obtain the fused image using the corresponding inverse transformation. The main classical component replacement methods are principal component analysis (PCA) [5; 6], GS transformation [7], HIS [8; 9], etc. The component substitution method is only applicable when the hyperspectral image and the panchromatic image are highly correlated; otherwise, it is prone to spectral distortion, although it must be noted that many scholars have improved the algorithm to address this shortcoming. [PERSON] et al. [10] extended the traditional IHS approach by proposing an adaptive IHS (AIHS) to adapt the coefficients of the IHS to preserve spectral information. [PERSON] et al. [11] proposed partial replacement adaptive component substitution (prs), which uses partial substitution to generate synthetic component images and then injects high-frequency information based on statistical scaling. The multi-scale decomposition-based image fusion method consists of three main steps: (i) first, multi-scale decomposition of multiple source images; (ii) then, fusion of the decomposition coefficients of different source images; and (iii) finally, multi-scale inversion on the fusion coefficients to obtain the fused image. Commonly used multi-scale decomposition methods include the Laplace pyramid [12], generalized Laplace pyramid (GLP) [13], discrete wavelet transform [14], etc. [PERSON] et al. [15] combined a multi-scale decomposition model with a free distribution model to better preserve spectral features. Compared with the component replacement method, the multi-scale decomposition method can better preserve the spectral information of the fused image and effectively solve the problem of spectral distortion, but due to the fusion strategy, the problem of spatial information loss will occur. Along with multi-scale decomposition and component substitution, another area of intense attention among academics is sparse representation theory-based null-spectrum fusion.