## 4 Experimental Results  
### Subjective Evaluation  
In this study, three images with different distances were selected for experimental processing, and the three images were taken from the following distances: within 200 m, from 200 m to 1000 m, and above 1000 m. The experimental results for all three images demonstrate that the quality is significantly improved, proving the wide applicability of this algorithm research.  
First, this study adopts different \(\gamma\) values to process infrared images for controlled experiments. Specifically, the \(\gamma\) values were set to 0.5, 1.2 and 2. The experimental results are shown in Figure 12. It can be observed from the figure that when the \(\gamma\) value is set to 0.5 and 2, as shown in Figure 12a,c, the details of the objects in the yellow box, especially the clothing stripes, become blurred, and the detail information is lost. However, after many repeated experiments, when \(\gamma\) is set to 1.2, as shown in Figure 12b, the details of the figure and the horse in the yellow box are better displayed, showing some enhancement effects. Meanwhile, this study also introduces an innovative gamma-taking formula, as shown in Figure 12d, and applies it in image processing. This innovative formula also enhances the detail information of the characters and horses in the yellow box, further confirming the applicability of the algorithm and showing the importance of adaptive \(\gamma\) for image processing.  
In addition, this study also carries out an experimental processing of HE algorithm, CLAHE algorithm, wavelet transform and adaptive gamma transform. Through comparisons, it is fully demonstrated that combining the time-frequency domain methods to process the image has significant advantages. A comparison of the experimental results shows that the algorithm in this study adopts layered processing and applies CLAHE to process low-frequency information, which effectively avoids the noise amplification caused by the HE algorithm and CLAHE algorithm directly processing the whole image. At the same time, through contrast enhancement, the HE algorithm has excessive enhancement problems in the processing of images, while the CLAHE algorithm, effectively avoiding the problem of over-enhancement, also results in a loss of detail information. The algorithm in this study can effectively solve the problem of information loss by layering the details in the high-frequency information layer. Wavelet transform and adaptive gamma processing lead to a certain improvement in the image brightness. However, the enhancement in contrast is small, and the visual effect is poor. In addition there is no obvious enhancement in the subsequent image processing. Therefore, the algorithm proposed in this study achieves  
Figure 12: Experimental results for different \(\gamma\) values: (**a**) Algorithm in this study and \(\gamma\) = 0.5, (**b**) Algorithm in this study and \(\gamma\) = 1.2, (**c**) Algorithm in this study and \(\gamma\) = 2, (**d**) Algorithm in this study and adaptive \(\gamma\).  
certain improvements in image richness, contrast and detail information compared to other methods and achieved good results. The experimental results of its algorithm are shown in Figure 13.