# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments
### Experimental Details  
Along with histogram equalization, we considered contrast-limited adaptive histogram equalization, which improves the visual quality of satellite images when combined with gamma correction. Gamma correction, as a nonlinear transformation algorithm, modulates luminance distribution by adjusting the gamma parameter (\(\gamma\)), where the selection of \(\gamma\) values critically determines the image enhancement performance. Given that a fixed \(\gamma\) value may not adequately accommodate varying lighting conditions, two parameters with better effects are selected for comparative experiments (\(\gamma=0.6\), \(\gamma=0.8\)) through experimental verification and theoretical analysis. Additionally, an adaptive gamma correction method [30] is incorporated for visual comparison, ensuring the rigor and scientific validity of the experiment.  
Figure 11: Comparison of low-light images in the SPEED-UE-Cube dataset before and after processing.  
In this experiment, we consider both single-scale and multi-scale Retinex algorithms [13], which are grounded in Retinex theory. For the single-scale Retinex algorithm, the Gaussian blur parameter is set to \(\sigma\) = (0.1, 0.1), while the multi-scale Retinex algorithm employs three parameters: \(\sigma\) = (15, 15), \(\sigma\) = (80, 80), and \(\sigma\) = (200, 200). For the deep learning methods, NeRCo and RetinexFormer, the experiments were conducted according to the official requirements. The NeRCo utilizes a pre-trained weight model on the LSRW dataset [31], while RetinexFormer uses the MST_Plus_Plus_NTIRE_4x1800 [14] weight model. The experiments were executed on a Linux system, with models built using version 1.10 of the PyTorch deep learning framework, and performed on an NVIDIA GeForce RTX 4090 graphics card.  
These methods have been validated using the test set from the LOL dataset [16] of natural scenes. The methods demonstrate both reliability and effectiveness. Figure 12 illustrates the visual comparative results of image enhancement on the LOL test set utilizing the aforementioned methods.  
This paper presents a multi-image fusion enhancement method tailored for low-light images with deep-space backgrounds. The core experimental methods and detailed procedures have been presented in Chapter 3. The key parameters, listed in Table 2, have been validated and optimized using a large dataset of deep-space background images, demonstrating high robustness and practical applicability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Basis for Selection** \\ \hline Exponential parameter p & 1.6 & Experimentally determined index parameters that balance contrast and detail retention. \\ Segment threshold m & 155 & Determined from the brightness characteristics of the target and background in \\  & & satellite images. \\ OTSU grayscale limit range & [20; 50] & Experiments show that satellite image noise is mostly concentrated in the low gray area, \\  & & and limiting the range can improve the threshold stability. \\ Percentage of regional expansion & 5\% & Balancing target integrity protection with over-extension risk. \\ Density thresholds & 0.8 & Ensure that the selection area has a compact form to avoid loose noise interference. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key parameters in the experiment.  
Figure 12: Visual comparison of different image enhancement methods in natural scenes.