## 5 Summary  
Aiming to resolve the problems of high dynamic infrared images, such as low contrast, high noise and blurred details, this study proposes an innovative algorithm to enhance the contrast enhancement and details of high dynamic infrared images. First, the discrete wavelet transform is studied in depth through theoretical analysis, the image is processed in layers, and the enhancement operation is carried out for the high-frequency information layer image and the low-frequency information layer image, separately, to improve the image quality. After wavelet transform, the low-frequency image mainly contains the background information of the original image, and the CLAHE algorithm is used to enhance the contrast. The high-frequency information layer image contains the detail and contour information of the original image. Adaptive gamma transform was designed in this study to adjust the gain coefficients so as to enhance the detail information of the image, and the parameters can be adaptively determined according to the characteristics of the image, which improves the applicability of the algorithm. The algorithm used in this study is further compared with the separate time-frequency domain algorithm and evaluated using a series of quantitative metrics. The results show that this study's algorithm performs well in terms of both visual effects and objective metrics, which confirms the potential application of the combined time-frequency domain approach to high dynamic infrared image enhancement. The algorithm in this study provides an effective research approach to acquiring high-quality infrared images, which provides strong support for future infrared \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Processing Method** & **Information Entropy** & **MSE** & **PSRN** & **SSIM** & **Processing Time (s)** \\ \hline Algorithm in this study and \(\gamma=0.5\) & 6.4769 & 0.0168 & 65.8812 & 0.6846 & 2.6231 \\ \hline Algorithm in this study and \(\gamma=1.2\) & 6.6945 & 0.0086 & 68.7359 & 0.8141 & 2.6079 \\ \hline Algorithm in this study and \(\gamma=2\) & 6.6273 & 0.0105 & 67.9009 & 0.7227 & 2.6727 \\ \hline Algorithm in this study and adaptive \(\gamma\) & 6.7438 & 0.0048 & 69.7825 & 0.8820 & 2.8223 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Objective indicators for controlled experiments with different \(\gamma\) values. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Processing Method** & **Information Entropy** & **MSE** & **PSRN** & **SSIM** & **Processing Time (s)** \\ \hline HE algorithm & 5.5140 & 0.0831 & 58.9346 & 0.5846 & 2.7564 \\ \hline CLAHE algorithm & 5.8949 & 0.0128 & 67.0569 & 0.6227 & 3.1723 \\ \hline Wavelet & 6.1175 & 0.0021 & 71.8331 & 0.9141 & 2.7345 \\ \hline Adaptive \(\gamma\) in this study & 6.1021 & 0.0070 & 72.1378 & 0.9379 & 3.4582 \\ \hline Algorithm in this study & 6.7438 & 0.0048 & 69.7825 & 0.8820 & 2.8223 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of objective indicators. image applications. In addition, future work can focus on further improving the performance of the algorithm and investigating the real-time and computational efficiency of the algorithm to ensure its practical feasibility in different application scenarios. This will help to promote further research and further applications in the field of high dynamic infrared image processing. Conceptualisation, D.Z. ; methodology, D.Z. ; software, D.Z. ; validation, D.Z. ; formal analysis, D.Z. ; investigation, D.Z. ; resources, D.Z., Y.Z., J.L. and B.S. ; data curation, D.Z. ; writing--original draft preparation, D.Z. ; writing--review and editing, D.Z., Y.L., Y.Z., J.L., S.C. and B.S. ; visualisation, D.Z. ; supervision, Y.L. ; project administration, Y.L. ; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript. Not applicable. The data presented in this study are available in the article.