It has been proven that the refractive index is related to meteorological parameters in physics. The temperature changes the atmospheric and lens refractive indices, resulting in image degradation. Image restoration aims to recover the sharp image from the degraded images. It is also the basis of many computer vision tasks. A series of methods have been explored and used in this area. Sometimes, meteorological factors cause image degradation. Most of the existing image restoration methods do not consider meteorological factors' influence on image degradation. How meteorological factors affect image quality is not yet known. A multispectral image dataset with corresponding meteorological parameters is presented to solve the problem. We propose a novel multispectral image restoration algorithm using global iterative fusion. The proposed method firstly enhances image edge features through spatial filtering. Then, the Gaussian function is used to constrain the weights between each channel in the image. Finally, a global iterative fusion method is used to fuse image spatial and spectral features to obtain an improved multispectral image. The algorithm explores the impact of meteorological factors on image quality. It considers the impact of atmospheric factors on image quality and incorporates it into the image restoration process. Extensive experimental results illustrate that the method achieves favorable performance on real data. The proposed algorithm is also more robust than other state-of-the-art algorithms. In this paper, we present an algorithm for improving the quality of multispectral images. The proposed algorithm incorporates the influence of meteorological parameters into the image restoration method to better describe the relationship between different spectral channels. Extensive experiments are conducted to validate the effectiveness of the algorithm. Additionally, we investigate the impact of near-surface meteorological parameters on multispectral image quality.