# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 2 Related Work  
Low-light image enhancement has been widely studied in the field of computer vision, especially for low-light images in natural scenes, and many methods have made significant progress. Low-light image enhancement methods for natural scenes can be categorized into two main groups: traditional methods and deep learning-based methods. Traditional methods mainly rely on histogram transformation, illumination estimation, and contrast adjustment, including histogram equalization [6], contrast-constrained adaptive histogram equalization (CLAHE) [10], gamma correction [9], and Retinex theory-based enhancement methods [11; 12; 17; 18]. With the advancement of deep learning, numerous models have been used to enhance low-light images [19; 20; 21]. For instance, [PERSON] et al. [14] proposed to combine Retinex theory with a Transformer to create a one-stage Retinex-based Transformer for low-light image enhancement. These methods can effectively improve image quality in natural scenes, enhance detail and color reproduction, and have better adaptive capabilities. Histogram equalization, contrast-limited adaptive histogram equalization (CLAHE), gamma correction, and similar methods enhance low-light images by adjusting brightness and contrast. While these methods can improve the visual appearance of images to some extent, they often fail to adequately account for the influence of lighting conditions. As a result, a noticeable gap remains between the enhanced image and one captured under the actual lighting conditions [14]. Enhancement methods based on Retinex theory improve low-light images by modeling the human visual system to produce results that align with human perception [17; 18]. However, these methods have two main limitations. First, they often assume that the input images are clean and free of noise artifacts. In practice, low-light images--particularly those with deep-space backgrounds--typically suffer from low signal-to-noise ratios, which can result in amplified noise after processing. Second, these methods rely heavily on manually designed prior assumptions about the image and often require extensive parameter tuning. As a result, they struggle to effectively handle the diverse and complex lighting conditions commonly present in low-light imaging scenarios [14; 15]. In recent years, deep learning has made significant progress in the field of low-light image enhancement [16; 19; 21]. By learning from large-scale datasets, deep learning-based methods have greatly improved the adaptability of enhancement techniques, enabling more effective preservation of image details under diverse lighting conditions. Several advanced models have demonstrated strong performance on benchmark datasets. For example, NeRCo [15] is an implicit neural representation specifically designed for collaborative low-light image enhancement. It addresses the limitations of traditional approaches by integrating neural representation learning with multimodal information. This design not only enhances detail preservation and denoising performance but also significantly improves the model's generalization ability. In contrast, RetinexFormer [14] is a Transformer-based low-light enhancement model grounded in Retinex theory. It incorporates a light estimation network with an attention mechanism to guide enhancement. This approach effectively recovers fine details in dark regions, suppresses noise, and improves overall image quality. While traditional methods and deep learning methods have produced satisfactory results in low-light conditions for natural scenes, they struggle to perform well when addressing deep-space background targets. This difficulty arises from the unique characteristics of the space environment. Specifically, the lighting conditions and background noise in the space environment are significantly more complex than those on the Earth's surface [4]. The absence of stable light sources, along with the presence of strong noise [2; 5] and highly variable illumination, poses substantial challenges.