With the gradual expansion of human activities into space, Earth's outer space, especially its geosynchronous orbit, is becoming increasingly crowded. A large amount of space debris is generated from abandoned space equipment and space activity waste. Scattered space debris may cause space accidents, leading to damage or derailment of space equipment. Therefore, space object detection systems are of great significance for ensuring the safety of the space environment. Stellar image preprocessing can improve image quality and target signal-to-noise ratio (SNR), which is significant for space target recognition, space target tracking, spacecraft navigation, and spacecraft attitude determination. This study mainly focuses on image denoising, background correction, threshold processing, and centroid extraction. The existing methods and their advantages and disadvantages are summarized, and the corresponding improvement methods are proposed. For image denoising and background correction, different algorithms are validated using a real stellar image. Additionally, the processing effects are analyzed using SNR gain and background suppression factor, and the effect for the targets with different SNRs are analyzed. Consequently, the neighborhood maximum filtering and improved background correction methods are proposed. In the threshold processing section, we analyze the histogram characteristics of real stellar images and propose an iterative adaptive threshold method based on them. For centroid extraction, we use Gaia data to generate a simulated stellar image based on the Gaussian point spread function. After adding white noise, we analyze the sub-pixel centroid extraction error and calculation time of different algorithms. Finally, based on the study results, the urgent need for future space target recognition is pointed out, and relevant suggestions are proposed.