# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 3 Method
### Enhancement of Single Satellite Images  
For enhancing single satellite images, the method extracts the satellite target region from the adaptively enhanced images and fuses it with the deep-space background portion of the original images. This approach enhances the satellite target while preserving detailed context about the dark parts of the deep-space background, thereby significantly improving the overall quality of the satellite image against the deep space. Figure 2 illustrates the workflow for the single satellite image enhancement process. Figure 1: The architecture of low-light image enhancement methods for deep-space background. After preprocessing the original image using RetinexFormer, noise and artifacts introduced during this process are eliminated through techniques like contrast enhancement based on segmented exponential functions. Subsequently, satellite targets are extracted from the enhanced image to generate a target mask, and then reverse masking is performed to obtain a mask of the deep-space background region. Following this, the background-masked areas of the original image (corresponding to the deep-space background) are fused with the satellite target regions from the adaptive enhanced image. This method effectively enhances the detailed features of the satellite, reduces noise, expands the visible area of the target, and significantly improves the overall image quality. 1. Contrast enhancement based on segmented exponential function  
To address the common problem of low contrast in satellite imagery, we utilize a contrast enhancement strategy that employs a segmented exponential function. This approach allows us to adjust the contrast of the input image effectively. The input image, denoted as \(I\in[0,\ 255]\)\({}^{W\times H\times 3}\), is normalized by RGB channels using the following equation:  
\[\hat{I}_{c}(x,y)=\frac{I_{c}(x,y)}{255},\ c\in\{B,G,R\} \tag{1}\]  
where \(I_{c}\) represents the pixels of the input image, and \(\hat{I}_{c}\) signifies the pixels after normalization. Subsequently, each color channel undergoes segmentation for contrast adjustment using the following adjustment formula:  
\[{I_{c}}^{\prime}(x,y)= \begin{cases}n\cdot\left(\frac{I_{c}(x,y)}{n}\right)^{p }&if\ I_{c}(x,y)<m\\ 1-(1-n)\cdot\left(\frac{1-\hat{I}_{c}(x,y)}{1-n}\right)^{p}&if\ I_{c}(x,y)\geq m \end{cases}  \tag{2}\]  
where \({I_{c}}^{\prime}\) denotes the pixel value of the adjusted image, m signifies the segmentation threshold, p represents the exponential parameter, and n is the value post-normalization of the threshold. By enhancing the dark region and suppressing the bright region, the low-brightness portion and the high-brightness portion of the image are adjusted to varying extents, thereby enhancing the image's contrast and rendering its details more discernible. 2. Detecting luminance distribution and adjusting dynamic threshold  
To further optimize the brightness distribution of the image, the image is converted to the HSL (Hue, Saturation, Lightness) space. The lightness component (L) is then extracted and analyzed using the lightness channel histogram. Subsequently, an adaptive gamma correction model is constructed based on this analysis. The brightness threshold \(\tau\) is determined by calculating the cumulative distribution function (CDF). This threshold is utilized to assess whether the brightness distribution of the image is suitable. If the image is found to be excessively dark or bright, adjustments are made accordingly. In addition, a dynamic contrast adjustment algorithm based on image brightness is designed. When the bright part of the image occupies a higher percentage, the contrast  
Figure 2: The workflow for the single satellite image enhancement process. adjustment is skipped; otherwise, the image is dynamically adjusted according to the calculated brightness threshold to further enhance the image details.