# Dual Histogram Equalization Algorithm Based on Adaptive Image Correction
## 4 Analysis of Algorithm Results
### Evaluation Results  
In this study, the performance of each algorithm is measured using image evaluation metrics such as SSIM, E, PSNR, AMBE, and time. As can be seen in Figures 4-13, the HE algorithm shows an obvious phenomenon of image brightness change and detail loss. Under low light conditions, the BBHE, RSIHE, and MMBEBE algorithms can protect the average brightness but will lead to uneven histogram balance due to unreasonable histogram segmentation, which is less effective for detail processing and will cause local area distortion. CLAHE can protect the image details but will introduce a large amount of noise, especially in Figures 10 and 13 where the image visual effect is significantly reduced. The AICHE algorithm improves the image contrast under the condition of protecting the average brightness of the image; it does not introduce excessive noise, and, to a certain extent, it retains the original shape of the original histogram. From Tables 1 and 2, the AICHE algorithm has the highest PSNR value and the SSIM value is closest to 1.The image information entropy in Table 3 demonstrates the richness of details in the image. The information entropy of the image processed by CLAHE obviously exceeds that of the original image, which indicates that noise is introduced in the image and produces a block effect. Except for the CLAHE algorithm, the AICHE algorithm has the highest information entropy value, indicating that the gray level merging phenomenon is effectively avoided in the equalization process, which can protect the image information. ESIHE cannot maintain the average image brightness well, which is also clearly reflected by the AMBE values in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Image** & **HE** & **BBHE** & **CLAHE** & **BPLHE** & **RSIHE** & **ESIHE** & **MMBEBHE** & **AICHE** \\ \hline Scene 1 & 0.35355 & 0.81761 & 0.65532 & 0.96649 & 0.89335 & 0.76816 & 0.87611 & 0.97822 \\ Scene 2 & 0.56202 & 0.82984 & 0.66354 & 0.86973 & 0.89335 & 0.90575 & 0.83181 & 0.91081 \\ Scene 3 & 0.40267 & 0.83725 & 0.47892 & 0.88366 & 0.87225 & 0.70318 & 0.87699 & 0.89607 \\ Scene 4 & 0.65454 & 0.94258 & 0.65375 & 0.77875 & 0.95223 & 0.87196 & 0.86033 & 0.95223 \\ Scene 5 & 0.58603 & 0.84615 & 0.63251 & 0.75727 & 0.88304 & 0.90293 & 0.84839 & 0.92898 \\ Scene 6 & 0.57518 & 0.72178 & 0.63395 & 0.85655 & 0.81686 & 0.83812 & 0.85081 & 0.89026 \\ Scene 7 & 0.79064 & 0.89123 & 0.63185 & 0.95572 & 0.89463 & 0.9434 & 0.89365 & 0.95629 \\ Scene 8 & 0.70979 & 0.88585 & 0.76452 & 0.77475 & 0.81018 & 0.89923 & 0.71782 & 0.91879 \\ Scene 9 & 0.90019 & 0.94821 & 0.91324 & 0.96226 & 0.97794 & 0.96686 & 0.92514 & 0.97873 \\ Scene 10 & 0.68766 & 0.77379 & 0.69663 & 0.83925 & 0.78948 & 0.83556 & 0.70165 & 0.85255 \\ \hline Average value & 0.62222 & 0.84942 & 0.67242 & 0.86444 & 0.87833 & 0.863515 & 0.83827 & **0.926293** \\ \hline Standard deviation & 0.16538 & 0.07077 & 0.11052 & 0.07907 & 0.06007 & 0.08025 & 0.07265 & 0.04078 \\ \hline \hline \end{tabular} Note: The top averages produced by the compared algorithms are marked in bold font.  
\end{table} 
Table 1: SSIM of different algorithms.