# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 3 Method
### Enhancement of Satellite Images Containing Celestial Bodies
#### 3.2.1 Extract the Earth Region
The comparison effect of the proposed method in this paper with the OTSU [22] algorithm for the binarization of images is shown in Figure 6. The proposed method can display the Earth and satellite target regions more completely, for the extraction of the Earth and satellite target regions, to avoid the loss of some regions. Since the Earth's surface has different features such as oceans, land, clouds, etc., if there is a part of the region where the feature brightness is not up to the average brightness, its region will be mistaken for the background and assigned a value of 0 (black) during binarization, resulting in a part of the Earth's region being missing. Two aspects are designed to prevent this from happening to ensure the integrity of the Earth's regions. On the one hand, the multi-segment threshold segmentation algorithm based on average luminance is designed to adjust the threshold effectively, minimizing the loss of dark information. On the other hand, known pixel areas and image sizes are computed to recover small black areas. If one of the following conditions is satisfied, it will be recovered:  
* The black area is entirely encircled by the white area, and its area is less than 50% of the white area;
* The region is located at the edge of the image, i.e., at least one point of its outline is located at the left, right, top, or bottom edge of the image. 2. Suppressing the halo effect  
Based on the physical characteristics of the halo, its luminance distribution satisfies \(T_{\text{halo\_low}}\leq I(x,y)\leq T_{\text{halo\_high}}\). According to the formula for converting RGB to YUV (Y represents luminance), the range of halo brightness of a set of images is obtained, and the thresholds \(T_{\text{halo\_low}}\) and \(T_{\text{halo\_high}}\) are determined to eliminate the influence of halo. \[\left\{ \begin{array}{l}Y=0.299\times R+0.587\times G+0.114\times B\\ U=-0.169\times R-0.331\times G+0.5\times B+128\\ V=0.5\times R-0.419\times G-0.081\times B+128\end{array} \right. \tag{9}\]  
where Y denotes luminance, which represents the light and dark information of an image, U denotes Blue-difference Chroma, which represents the difference between blue and luminance in the color information, and V denotes Red-difference Chroma, which represents the difference between red and luminance in the color information. To perform halo separation, a double threshold segmentation is applied to generate a halo mask, and a logical XOR operation is used to eliminate the interference of the halo on the target area. The operation formula is as follows:  
\[B_{\text{split}}(x,y)=B_{\text{raw}}(x,y)\oplus B_{\text{halo}}(x,y) \tag{10}\]  
Figure 6: The comparison effect of our proposed method with the OTSU for binarization of images. where \(B_{\text{halo}}(x,y)\) represents the binary image of the extracted part of the halo, \(B_{\text{raw}}(x,y)\) represents the initial binary image, and \(B_{\text{split}}(x,y)\) represents the binary image after separating the halo. The halo suppression strategy adopted in this paper is based on the dual threshold (\(T_{\text{halo\_low}}\) and \(T_{\text{halo\_high}}\)) setting to identify and remove halo areas. This method has certain limitations in practical applications, mainly reflected in the sensitivity of threshold selection: when the brightness of the Earth or deep-space background falls into the \(\{T_{\text{halo\_low}},T_{\text{halo\_high}}\}\) interval, it may be misjudged as a halo area and mistakenly removed. In order to reduce misidentification, we usually adopt a more conservative strategy in design, that is, appropriately increase \(T_{\text{halo\_low}}\) and reduce \(T_{\text{halo\_high}}\), so that the range of \(\{T_{\text{halo\_low}},T_{\text{halo\_high}}\}\) is slightly smaller than the actual halo brightness interval. This approach improves the accuracy of halo extraction and reduces misidentification, but in some scenarios, such as when the halo brightness is close to the background or the main body of the Earth, some halo areas may not be completely suppressed. 3.