Abstract: Astronomical imaging technologies are basic tools for the exploration of the universe, providing basic data for the research of astronomy and space physics. The Soft X-ray Imager (SXI) carried by the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) aims to capture two-dimensional (2-D) images of the Earth’s magnetosheath by using soft X-ray imaging. However, the observed 2-D images are affected by many noise factors, destroying the contained information, which is not conducive to the subsequent reconstruction of the three-dimensional (3-D) structure of the magnetopause. The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models. This makes it difficult to establish the mapping relationship between SXI-simulated observation images and target images by using mathematical models. We propose an image restoration algorithm for SXI-simulated observation images that can recover large-scale structure information on the magnetosphere. The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image, whose mapping relationship with the target image is established by the patch estimator. The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator. Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task, according to the peak signal-to-noise ratio and structural similarity. The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques, significantly improving the reconstruction results. Hence, the proposed technology may be feasible for processing SXI-simulated observation images.