## 3 Results and Discussion
### Infrared Image Denoising Experiments  
The configuration and parameters of the processing platform of the identification system are shown in Table 4, and the model and main parameters of the infrared camera are shown in Table 5.  
The WTD-IFA algorithm and the remaining five different denoising algorithms are used to denoise the infrared image and denoising results are shown in Figure 8, where FOA refers to fruit fly optimization algorithm. In order to evaluate the denoising effect more objectively, the information entropy of the image and the energy gradient function of the image are used as evaluation indicators. The information entropy of the image is a feature statistical form, which reflects the average amount of information in the image, so the entropy can be used as a focus evaluation standard. In general, the higher the entropy is, the clearer the image will be. The energy gradient of the image accumulates all pixel gradient values as definition evaluation values, so the larger the energy gradient value is, the clearer the image will be. The results of the denoising evaluation are shown in Tables 6 and 7.  
It can be seen from Table 6 that the Block-Matching 3-Dimension (BM3D) algorithm obtains the maximum entropy value of image information, and the WTD-IFA proposed in this paper is closely followed by it, with suboptimal results. The median filtering algorithm causes the information entropy of the image to drop, the remaining algorithms increase the information entropy of the image. The denoising effects of the three adaptive algorithms based on wavelet threshold are similar, and the improved WTD-IFA algorithm in this paper can achieve better results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Parameter** \\ \hline Infrared camera brand & Juge Electronics \\ Detector type & Uncooled focal plane \\ Wavelength range & 7.5–14 μm \\ Pixels & \(384\times 288\) \\ Frame rate & 50 Hz \\ Operating temperature & \(-30\)\(\sim\)\(60\)\({}^{\circ}\)C \\ Temperature measurement range & 0–300 \({}^{\circ}\)C \\ Temperature measurement accuracy & 2 \({}^{\circ}\)C \\ focal length & 10 mm \\ Angle of view & \(37.4^{\circ}\times 28^{\circ}\) \\ Angular resolution & 1.7 mrad \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of online thermal imaging camera.  
Figure 7: Infrared images of five working conditions.  
Another evaluation index selected in this paper is the energy gradient function of the image and the comparison results are shown in Table 7. The top three algorithms are BM3D, bilateral filtering and the algorithm proposed in this paper, and the image energy gradient of the three algorithms is obviously superior to other algorithms. The denoising effect of the bilateral filtering algorithm is similar to that of the three adaptive algorithms based on the wavelet threshold. The median filtering with the poor denoising effect cannot meet the system requirements. In contrast, the BM3D algorithm has the best denoising effect.  
Figure 8: _Cont._  
Figure 8: Image denoising results based on different algorithms.  
In order to better prove the superiority of the algorithm, this paper records the processing time of the algorithm mentioned, as shown in Table 8. Combined with the results of Tables 6 and 7, it is concluded that BM3D algorithm has obtained the optimal results in terms of information entropy and energy gradient, but its processing time is longer. The algorithm proposed in this paper has the same processing time as other algorithms, and has achieved excellent results in the indicators of information entropy and gradient, which is more competitive than other comparison algorithms in solving image denoising problems.  
In summary, the WTD-IFA algorithm proposed in this paper has a good denoising effect and a fast running speed, which can meet the requirements of pipeline compressor air leak identification systems.