# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments
### Visualization (Qualitative Comparison)  
The original image serves as a reference for comparing images enhanced through various methods. Figure 15 provides a localized visual comparison with two state-of-the-art deep learning methods. A comparative analysis of the local views of satellite targets enhanced by different methods reveals that while the deep learning-based approaches, NeRCo and RetinexFormer, enhance satellite targets to some extent, they also introduce excessive noise during the enhancement process, ultimately degrading visual quality. In contrast, the proposed method not only effectively preserves and enhances fine details but also demonstrates superior capability in suppressing noise and artifacts, achieving significantly better visual results than the other two methods.  
Figure 15: Localized visual comparison with two state-of-the-art deep learning methods.  
Figure 19 presents a visual comparison of multiple enhancement methods on a satellite image containing the Earth region. Subsequently, we conduct a qualitative analysis.  
Figure 19: Visual comparison with multiple methods on satellite images containing Earth region.  
Figure 18: Visual comparison of single satellite images before and after enhancement.  
The histogram equalization method effectively enhances the satellite target and presents space target information; however, it introduces excessive noise and artifacts, causing significant distortion in the color and brightness of the Earth region. The single-scale and multi-scale Retinex methods provide slightly improved visual results compared to histogram equalization but still introduce considerable noise and artifacts.  
The gamma correction method, with a \(\gamma\) value of 0.6, enhances target details to some extent. When combined with the CLAHE method, it further improves detail clarity; however, it also amplifies the Earth halo effect, leading to a reduction in overall image quality. Conversely, when \(\gamma\) is set to 0.8, the enhancement effect is limited, yielding under-enhancement results. The adaptive gamma correction method improves satellite target visibility but introduces significant noise, compromising image quality. Among the deep learning-based approaches, NeRCo and RetinexFormer enhance satellite targets and improve texture details; however, they also introduce substantial noise into the deep-space background, adversely affecting the overall visual quality. In contrast, the proposed method not only enhances satellite targets while preserving fine details but also effectively suppresses interference from the Earth halo and minimizes the generation of noise and artifacts, ultimately achieving superior enhancement performance.  
Figure 20 presents a visual comparison of single satellite images processed using various enhancement methods. Among these methods, histogram equalization produces the poorest results, causing severe distortion of the satellite target and introducing substantial noise, which significantly degrades image quality. Adaptive histogram equalization performs slightly better; however, it still introduces considerable noise and artifacts during the enhancement process.  
Figure 20: Visual comparison with multiple methods on the single satellite target image.  
Both the single-scale and multi-scale Retinex methods enhance satellite targets to some extent but also introduce a significant amount of noise. The gamma correction method achieves better performance compared to the aforementioned methods, yet it still introduces a slight amount of noise. The deep learning-based NeRCo method demonstrates suboptimal performance, as it enhances the target image while generating excessive noise, ultimately reducing image quality. The RetinexFormer method performs better than NeRCo but still introduces some noise during the enhancement process. In contrast, the proposed method achieves a better balance between target enhancement and noise suppression, effectively improving image quality.