# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 3 Consistency Processing Method
### Color Consistency Framework
#### 3.1.2 Multi-View Consistency Process  
After the single-view image processing, the multi-view color consistency processing adjusted the color distribution between the different images. The task was mosaic preprocessing, and the mosaic line calculation was based on the consistent multi-view results. However, there was no mosaic line information available for the processing. Therefore, some of the color consistency processing methods using mosaic line elimination [26] were not appropriate for this task. The principle of image statistics was considered, in that the color statistics such as mean, variance, etc., could characterize the differences between different images and could be reduced to ensure the consistency between different images [27; 28; 29]. Color transfer [30] is an effective method of color consistency adjustment. In the proposed method, a color transfer method was introduced. According to the results of the color space conversion, the mean and standard deviation values of the images were extracted, and linear mapping was performed based on these statistical results to achieve coherent color results. The multi-view consistency could be achieved by color space conversion and normalization of the features. The complete multi-view consistency processing flowchart is shown in Figure 8.  
Figure 8: The flowchart of multi-view color consistency processing. \(RGB\) to \(L\alpha\beta\) color space  
The data correlation of the three channels in the \(RGB\) color space was very strong. The \(L\alpha\beta\) color space proposed by [PERSON] et al. [31] eliminated the strong correlation between color channels and effectively separated the color information from the image. The color space conversion process is as follows:  
\[ \begin{pmatrix}L\\ M\\ S\end{pmatrix} = \begin{pmatrix}0.3811&0.5783&0.0402\\ 0.1967&0.7244&0.0782\\ 0.0241&0.1288&0.8444\\ \end{pmatrix}\begin{pmatrix}R\\ G\\ B\end{pmatrix} , \tag{14}\]  
\[ \begin{pmatrix}L^{*}\\ M^{*}\\ S^{*}\end{pmatrix} =\begin{cases} \begin{pmatrix}\log_{10}L\\ \log_{10}M\\ \log_{10}S\end{pmatrix} &if \begin{pmatrix}L\\ M\\ S\end{pmatrix} > \begin{pmatrix}0\\ 0\\ 0\\ 0\end{pmatrix} \\ \begin{pmatrix}L\\ M\\ S\end{pmatrix} &if \begin{pmatrix}L\\ M\\ S\end{pmatrix} \leq \begin{pmatrix}0\\ 0\\ 0\end{pmatrix} ,\\ \begin{pmatrix}L\\ \alpha\\ \beta\end{pmatrix} = \begin{pmatrix}\frac{1}{\sqrt{3}}&\frac{1}{\sqrt{3}}&\frac {1}{\sqrt{3}}\\ \frac{1}{\sqrt{6}}&\frac{1}{\sqrt{6}}&\frac{-2}{\sqrt{6}}\\ \frac{1}{\sqrt{2}}&\frac{-1}{\sqrt{2}}&0\\ \end{pmatrix}\begin{pmatrix}L^{*}\\ M^{*}\\ S^{*}\end{pmatrix} . \tag{15}\]  
\(\bullet\) Feature calculation  
To eliminate the effect of the background, auxiliary masks for the Antarctic continental part, as described in Section 3.1.1, were utilized to obtain the features of the images. The mean and standard deviation for the Antarctic continental part in the images are calculated as follows:  
\[MEAN=\frac{\sum f(x,y)}{n}, \tag{17}\]  
\[STD=\frac{\sqrt{(f(x,y)-MEAN)^{2}}}{\sum{mask_{auxiliary}(x,y)}}, \tag{18}\]  
where \(f(x,y)\) is the value of pixel \((x,y)\) and \((x,y)\in((x,y)\mid{mask_{auxiliary}(x,y)}=1)\). \(\bullet\) Feature normalization  
The averages of the means and the standard deviations of all the images were calculated as the normalized target values, and they are calculated as follows:  
\[MEAN_{target}=\frac{\sum MEAN}{I_{num}}, \tag{19}\]  
\[STD_{target}=\frac{\sum STD}{I_{num}}, \tag{20}\]  
where \(MEAN_{target}\) is the average of the means, \(STD_{target}\) is the average of the standard deviations, and \(I_{num}\) is the number of images. The normalizing process for the features is as follows:  
\[f_{result}(x,y)=(f(x,y)-MEAN)\times\frac{STD_{target}}{STD}+MEAN_{target}, \tag{21}\]  
where pixels participating in the calculation are \((x,y)\in((x,y)\mid{mask_{auxiliary}(x,y)}=1)\). * Results visualization  
The output images from the \(L\alpha\beta\) color space were converted into \(RGB\) for the final visualization. The color space conversion process is as follows:  
\[ \begin{pmatrix}L\\ M\\ S\end{pmatrix} =