# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 1 Introduction  
With the rapid advancement of space technology, monitoring space targets has become essential for missions such as space situational awareness [1], autonomous spacecraft navigation, and space debris management [2, 3]. However, imaging systems often encounter extremely low-light conditions and complicated background interference [4] when capturing space targets against deep space, leading to weak target signals that can easily be obscured by noise [2, 5]. Existing low-light image enhancement methods do not adequately consider the specificity of the space environment, including traditional enhancement methods (e.g., histogram equalization [6, 7, 8], gamma correction [9, 10], Retinex theory-based methods [11, 12, 13]) and deep learning-based models (e.g., RetinexFormer [14], NeRCo [15]), which are primarily designed for natural light scenarios and have been mainly applied in ground environments, both indoor and outdoor. Therefore, these methods have significant limitations when applied to low-light space scenes characterized by deep-space backgrounds. Enhancing target details in low-light images set against deep-space backgrounds, as well as effectively reducing the noise artifacts introduced by the enhancement, have become pressing challenges. Satellite images typically include weak light sources from distant stars, as well as scattered light in deep space [4]. However, current enhancement methods [6; 10; 14; 16] primarily focus on natural lighting scenes and often overlook weaker light sources, stray light, Earth halos, and other types of noise present in the space environment. Consequently, these methods serve to enhance the primary target while concurrently amplifying various forms of background interference, including but not limited to background noise, stray light, and atmospheric halos. This degradation adversely affects the visual quality of the image and can interfere with the accurate identification and assessment of targets, such as satellites. Additionally, the Earth itself plays a role in degrading image quality. When targets like artificial satellites are enhanced, the Earth's portion of the image is also enhanced, leading to color distortion and brightness aberrations, which seriously affect the subsequent visual analysis, target detection, and other tasks. To cope with the above problems, this study presents a low-light image enhancement method specifically designed for deep-space backgrounds, utilizing multi-image fusion. The method integrates spatial target features with Retinex theory [17], employing the RetinexFormer [14] deep learning model to preprocess the original image. After the preprocessing step, adaptive image enhancement is realized based on the luminance distribution detection mechanism to suppress and eliminate the noise, artifacts, etc., introduced during the preprocessing process. Subsequently, the satellite target region is extracted from the enhanced image, while the Earth region and deep-space background are extracted from the original image for multi-image fusion. This method effectively suppresses the Earth's halo, avoids the color and brightness distortion of the Earth, increases the visible area of the target satellite, and significantly improves the detail quality of the image. Meanwhile, the edge detection extension module is designed to effectively avoid incomplete extraction of the target region and solve the problem of missing parts of the satellite target. This study establishes an effective framework for improving the visual quality of spacecraft images and provides important technical support for applications such as spacecraft identification and pose estimation [1; 4], space target detection [2; 4; 5], space situational awareness, and accurate and efficient spacecraft docking [1; 2; 5]. The main contributions of this paper can be summarized as follows. 1.