# Extraction of Hidden Information under Sootiness on MuraIs Based on Hyperspectral Image Enhancement
## 5 Conclusions  
Extracting hidden information from damaged murals is a challenging task in cultural conservation. The objective of this study was to develop a new method of hidden information detection using hyperspectral imaging techniques. Hyperspectral images of a Guanyin Temple mural was used as the study case. A series of spectral analysis approaches was integrated and applied to enhance the visual expression of the hidden information, and the pattern under the sootiness was extracted from hyperspectral images effectively. This was done by applying MNF transformation to reduce the features of the scratches and sootiness in the mural background, and by spectral feature analysis and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area** & **Method** & \begin{tabular}{c} **Overall Accuracy** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Omission Error** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Commission Error** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{3}{*}{Area 1} & Proposed Method & 88.97 & 6.68 & 4.35 \\  & SAM & 84.72 & 13.83 & 1.45 \\  & SVM & 80.01 & 14.79 & 5.20 \\ \hline \multirow{3}{*}{Area 2} & Proposed Method & 87.50 & 9.54 & 2.96 \\  & SAM & 57.46 & 8.85 & 33.69 \\ \cline{1-1}  & SVM & 71.74 & 7.73 & 20.53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the different methods.  
Figure 12: The extraction results of the hidden information of area 1 with different methods. (**a**) Result of the proposed method; (**b**) result of the Spectral Angle Mapper (SAM); (**c**) result of the Support Vector Machine (SVM); extraction results of the hidden information of area 2 with different methods; (**d**) result of the proposed method; (**e**) result of the SAM; (**f**) result of the SVM.  
image subtraction to enhance the characteristics of ancient murals, which highlighted the blurred image of the mural under the sootiness and increased the readability and artistic expressiveness of the ancient murals. Finally, the pattern covered by the sootiness was extracted by density slicing. By adjusting the threshold, the overall accuracy reached 88.97%.  
However, as mentioned above, there are many kinds of hidden information, such as hidden patterns and text, underdrawings and manuscript, smeared information, and restoration marks. The proposed method is not suitable to all situations, because the spectral curves of different substances are also different from each other; to enhance the target information, a certain difference in the reflectance of the objective and other substances is required. Furthermore, the input of image subtraction needs to be analyzed according to the specific extraction object. Therefore, further studies are needed to deal with different types of occlusion and to adapt the method to different extraction targets.  
Conceptualization, M.H.; Data curation, C.X.; Methodology, M.H., N.C., L.T. and S.L.; Supervision, P.Z.; Writing--original draft, N.C. and L.T.; Writing--review & editing, N.C. and S.L.  
This work was supported by the Research Fund of the National key research and development program (No. 2017 YFB1402105), the Fundamental Research Funds for Beijing University of Civil Engineering and Architecture (No. X18024), and the Great Wall Scholars Training Program Project of Beijing Municipality Universities (CIT&TCD20180322).  
The authors declare no conflict of interest.