# Image Dehazing Enhancement Strategy Based on Polarization Detection of Space Targets
## 5 Image Dehazing Method Based on Full Polarization Information Inversion and Image Fusion
### Basic Principle of Image Dehazing Based on Full Polarization Information Inversion
#### 5.1.1 Inversion Method of Skylight Intensity  
The brightest pixel in the haze image or the gray value of the pixel at infinity in the sky area is usually selected as the skylight intensity in the traditional polarization differential dehazing method [23]. Although the skylight intensity can be obtained easily in this method, it will produce accidental errors. If the noise pixels of the image are high grayscale, there will be a large estimation deviation, and the image restoration result will be disturbed.  
In our paper, the threshold segmentation method is first used to separate the high-grayscale pixels in the image, the pixels in the atmospheric area, and the noisy pixels. The segmented image is used to obtain the atmospheric light value at infinity so as to reduce the influence of bright pixels and noisy pixels on the skylight estimation results. In the space target detection image, the atmospheric area occupies the main area of the entire image; in order to improve the processing efficiency of the method, the scale of the skylight area can be appropriately adjusted according to the relative position of the target in the image and the size of the image, larger scale values can help improve the accuracy of the estimation. The threshold segmentation method used in our paper is shown in Equation (20).  
\[Q(x,y)=\left\{\begin{array}{l}1\Bigg{|}I(x,y)-\dfrac{1}{|\Omega|}\sum\limits _{(x,y)\in\Omega}I(x,y)\Bigg{|}\leq 2\sigma\\ 0\Bigg{|}I(x,y)-\dfrac{1}{|\Omega|}\sum\limits_{(x,y)\in\Omega}I(x,y)\Bigg{|}> 2\sigma\end{array}\right. \tag{20}\]  
\(\Omega\) represents the image of the skylight area, \(\sigma\) represents the standard deviation of all pixels in the skylight area, and the threshold segmentation is performed by the \(2\sigma\) rule.  
When the threshold segmentation is completed, Equation (21) is used to remove the high-grayscale pixels and noisy pixels in the original image according to the threshold segmentation results.  
\[A(x,y)=Q(x,y)\times\underset{(x,y)\in\Omega}{I}(x,y) \tag{21}\]  
Figure 5: The image processing flowchart of the proposed method.  
Finally, the average value of the remaining pixels in the image is obtained as the value of the skylight intensity.  
1.2 Inversion Method of Differential Radiation Intensity and the Degree of Linear Polarization of Skylight  
In the space target polarization detection system, the polarization camera based on MPA is used, so polarization images with two different polarization directions can be directly obtained at the same time; combined with the \(S_{0}\) component in the Stokes vector, the differential radiation intensity can be calculated using Equation (22).  
\[\Delta I(x,y)=S_{0}(x,y)\times Dolp(x,y) \tag{22}\]  
In the process of space target detection, the distance between the equipment and the target can be regarded as an infinite distance, so the polarization of the skylight area in the image is calculated as the polarization value of the skylight. In this paper, the local pixels in the skylight area are selected, and the average value of these pixels is used as the estimation result of the degree of linear polarization of skylight, as shown in Equation (23).  
\[Dolp_{air}(x,y)=\frac{1}{|\Omega|}\sum_{(x,y)\in\Omega}Dolp(x,y) \tag{23}\]