# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments  
### Dataset Selection  
For satellite images, the existing relevant datasets include SPEED [26], SPEED+ [27], SPARK 2021 [4], SPE3R [28], and SPEED-UE-Cube [29], among others. These datasets are primarily utilized for spacecraft pose estimation, 3D Reconstruction, and similar applications. However, low-light enhancement datasets designed specifically for deep-space background targets are very scarce. Table 1 analyzes the available satellite image datasets, detailing the collected scenes, lighting conditions, background noise, bit depth, image resolution, and application types. Spacecraft Pose Estimation Dataset (SPEED) [26] is the first public benchmark dataset for spacecraft pose estimation and has become the most widely used satellite dataset in recent years. However, SPEED primarily addresses satellite pose estimation issues and does not account for illumination changes or background noise in space scenes. As a result, it is not suitable for low-light enhancement. In response to these limitations, [PERSON] et al. introduced SPEED+ (Next-Generation Spacecraft Pose Estimation Dataset) [27], which considers variations in lighting conditions and enhances the spacecraft pose estimation dataset. SPEED+ includes both synthetic images and hardware-simulated images. The synthetic images are created using OpenGL rendering, similar to the original SPEED, while the hardware-simulated images are generated on SLAB's Rendezvous and Optical Navigation (TRON) test bench. Lighting conditions for the images are simulated using two light sources: a light box and a sun lamp. Despite these improvements, SPEED+ still does not account for background noise in the space scene. The SPE3R [28] dataset consists of 3D watertight mesh models of 64 individual satellites from NASA 3D Resources and the European Space Agency's scientific satellite fleet. Although the dataset contains a rich variety of satellite types, the data are only used to support simultaneous 3D structure representation and pose estimation and still do not consider illumination changes and spatial background noise. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **SPEED** & **SPEED+** & **SPE3R** & **SPEED-UE-Cube** & **SPARK 2021** \\ \hline Scene & Synthetic & Synthetic and real & Synthetic & Synthetic & Synthetic \\ Illumination & No & Yes & No & Yes & Yes \\ BGD Noise 1 & No & No & No & Yes \\ Bit Depth & 8 & 8 & 24 & 32 & 24 \\ Resolution & 1920 \(\times\) 1200 & 1920 \(\times\) 1200 & 256 \(\times\) 256 & 1920 \(\times\) 1200 & 1024 \(\times\) 1024 \\ Categories & 4 & 4 & 64 & 1 & 11 \\ Application & Pose estimation & Pose estimation & Reconstruction & Pose estimation \\ \hline \hline \end{tabular}  
* BGD Noise denotes deep-space Background Noise.  
\end{table} 
Table 1: A comparison and analysis of existing datasets. Spacecraft Pose Estimation Dataset of a 3U CubeSat using Unreal Engine (SPEED-UE-Cube) dataset is derived from the SPEED and SPEED+ datasets and is specifically focused on modeling 3U CubeSats [29]. This dataset, which is rendered using Unreal Engine 5, contains airborne images of CubeSat models. However, this dataset is still used to solve the satellite attitude estimation problem and only contains images of 3U CubeSat satellites. Therefore, using this dataset for the validation of methods is not very convincing. In contrast, the SPARK 2021 dataset is a unique spatial multimodal annotated image dataset. The dataset contains deep-space background images of 10 representative artificial satellites, including AcrimSat, Calipso, CubeSat, and Jason. These data are generated in a realistic space simulation environment with a variety of sensing conditions, including multiple lighting conditions for different orbital scenes, complex cosmic background noise, etc. It is suitable for processing using low-light enhancement methods, so this paper selects deep-space images in the SPARK 2021 dataset for method verification.