# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 5 Conclusions  
Since the existing low-light enhancement methods do not consider the particularity of the space environment, they are not effective in processing deep-space background targets. To address this problem, this study proposes a deep-space background low-light enhancement method based on multi-image fusion, which effectively solves the problems of Earth halo interference and color distortion, and significantly improves the visibility of details of satellite targets while ensuring high fidelity of the Earth region. The method achieves adaptive image enhancement through image preprocessing, dynamic adjustment of contrast, brightness, and noise removal, effectively suppresses the influence of the Earth halo, retains the detailed information of deep-space background, and effectively improves the image quality of targets in deep-space background. Experimental results show that the proposed method is superior to traditional methods and mainstream deep learning models in qualitative and quantitative evaluation. The method is suitable for various types of satellite image processing, especially when processing satellite images with high noise and low contrast, and has a good enhancement effect. However, the method has some limitations, such as the inability to suppress the halo completely and to process scenes where the target overlaps with the Earth region. Figure 21: Average processing time of different satellite images at three resolutions. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Resolution** & **Metric** & **AcrimSat** & **Aquarius** & **Aura** & **Calipso** & **CloudSat** & **CubeSat** & **Jason** & **Sentinel-6** & **Terra** & **TRMM** \\ \hline \multirow{2}{*}{\(256\times 256\)} & Time (ms) & 71.65 & 61.92 & 73.77 & 69.14 & 67.89 & 71.84 & 69.50 & 57.45 & 68.28 & 67.99 \\  & Frame rate (FPS) & 13.96 & 16.15 & 13.55 & 14.46 & 14.73 & 13.92 & 14.39 & 17.41 & 14.65 & 14.71 \\ \hline \multirow{2}{*}{\(512\times 512\)} & Time (ms) & 87.12 & 94.85 & 95.53 & 92.51 & 86.31 & 92.01 & 89.06 & 86.07 & 82.51 & 93.83 \\  & Frame rate (FPS) & 11.48 & 10.54 & 10.47 & 10.81 & 11.59 & 10.87 & 11.23 & 11.62 & 12.12 & 10.66 \\ \hline \multirow{2}{*}{\(1024\times 1024\)} & Time (ms) & 188.25 & 189.84 & 184.89 & 183.39 & 184.22 & 195.37 & 194.13 & 185.04 & 178.80 & 192.84 \\  & Frame rate (FPS) & 5.31 & 5.27 & 5.41 & 5.45 & 5.43 & 5.12 & 5.15 & 5.40 & 5.59 & 5.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average processing time and frame rate (FPS) of satellite images at different resolutions. To address the limitations of existing halo suppression techniques, we plan to exploit spatial and contextual information modeling in future work. The current halo suppression method is mainly based on brightness threshold processing, ignoring the differences in spatial structure and texture features between the halo and the background in the image. To improve recognition accuracy, we plan to introduce a spatial context modeling mechanism, using super-pixel segmentation or edge-preserving region growing algorithm, combined with local texture direction, consistency, and other information, to accurately segment the halo edge area. Furthermore, we consider using graph neural networks (GNNs) to model the connection relationship between pixels or regions in the image. By modeling the image as a graph structure (such as a pixel adjacency graph or a super-pixel graph), each node represents a local region, and the weight of the edge is determined based on the spatial distance and feature similarity between regions, thereby constructing a weighted graph reflecting the internal structure of the image. The graph neural network is used to propagate contextual information on the structure and identify halo regions with continuity and diffusion characteristics. As part of our future work, we aim to address the challenge of limited deep-space background image data.