## 1 Introduction  
Since Sputnik-1 was launched in 1957, the pace of space exploration quickened [1, 2], and the number of targets in space (including satellites and space debris) has increased rapidly [3, 4]. According to the Union of Concerned Scientists' satellite database, as of 1 January 2022, there were 4852 satellites in orbit accompanied by a mass of space debris. The famous scientist [PERSON] pointed out that once these targets collide, every collision could trigger a cascade of collisions that create more space debris and pose a massive threat to operational satellites [5]. For example, space debris of about 10 cm in diameter can destroy any operational satellite it collides with, which would have severe implications for human space activities [6]. To address the growing demand for space exploration while also assuring the security of the space environment, countries all over the world are installing a strategic layout of space-based surveillance systems; for example, the Fengyun satellite program of China [7], the Space Surveillance System of Canada (CSSS), the Space-Based Space Surveillance system (SBSS) [8, 9], the Space Tracking and Surveillance System (STSS) and the Space-Based Infrared System (SBIRS) [10] of the USA. However, as space-based surveillance systems operate in outer space, most of the detected space targets are dim with high magnitude. Accordingly, stray light can have a severe impact on the detection performance of space-based surveillance systems [11]. In such cases, the surveillance image is the only data source. If stray light cannot be effectively suppressed, it will create a harsh nonuniform background signal in the surveillance image, reducing its clarity and dynamic range. Furthermore, substantial degradation effects can appear in surveillance images that greatly obstruct the subsequent recognition and detection of space targets [12; 13]. In conclusion, stray light noise badly affects the normal operation and detection ability of these systems. In addition, a surveillance image may represent a vast number of targets of various sizes [14]. Precisely eliminating nonuniform backgrounds generated by stray light without losing targets of various sizes is a challenge that remains. As a result, research on nonuniform background elimination methods for surveillance images is urgently needed. In recent years, the reduction of stray light has become a problem not only in the field of instrument measurement, but also in the preprocessing of faint target detection and tracking. At present, current nonuniform background elimination methods for surveillance images can be roughly divided into two types: a (1) parametric model-based method, and an (2) image feature-based method. In parametric model-based methods, the nonuniform background is eliminated by constructing a parametric model. Specifically, abundant images are collected in advance, then classified to create a training data set to build a parametric model, such as a spline function or polynomial model, or recent deep learning-based models [15; 16]. However, these algorithms are not very effective in eliminating nonuniform backgrounds from surveillance images. On the one hand, parametric models must be accurate, such as the order or the number of terms in the polynomial. However, in practice, there are huge differences in the distributions of nonuniform backgrounds in different images, which makes it difficult to accurately determine the parameters under different conditions or scenes. In addition, even if we have enough surveillance images for training a parametric model, there may still be some unknown cases that make it difficult for fixed-parameter models to eliminate nonuniform backgrounds from surveillance images that are not included in the dataset. On the other hand, some targets with low signal-to-noise ratios (SNRs) exist in surveillance images. Minor flaws in the parametric model development process could result in target losses or greater false alarm rates, lowering the accuracy of subsequent target recognition [17].