## 5 Experiment and Application
### Application  
At present, the video monitoring of coal winning machine under the mine is getting more and more attention. However, using the camera for video monitoring is challenged by low illumination and coal dust environment in the mining face. When cutting the coal wall, the cutting unit of the mining machine produces great heat and radiates infrared ray. Thus, in 2016, the infrared thermal imager was applied to monitor the coal winning machine in Zhangjiakou Coal Mining Machinery Co., Ltd., Zhangjiakou, China. The infrared thermal imager taking into use in mining face could acquire clearer images than camera, as shown in Figure 20C, and it was more likely to realize the video monitoring and tracking of coal mining machine. Figure 19: Denoising results of Peppers with Speckle noise using IFOA-DTCWT-BF. (**A**) Peppers of noise \(\sigma\) = 0.01; (**B**) Peppers of noise \(\sigma\) = 0.05; (**C**) Peppers of noise \(\sigma\) = 0.1; (**D**) Peppers of noise \(\sigma\) = 0.2; (**E**) Denoised image of \(\sigma\) = 0.01; (**F**) Denoised image of \(\sigma\) = 0.05; (**G**) Denoised image of \(\sigma\) = 0.1; (**H**) Denoised image of \(\sigma\) = 0.2. Figure 20: _Cont._However, the infrared thermal images have same unavoidable weaknesses. The low sharpness, noise problem and inhomogeneous are three major problems of the thermal images. Because of the low detectivity and spatial resolution of the thermal imaging system, the thermal images owns blurred visual effect. The random disturbances of the external environment like lamp light, dust, etc. cause the noise problem and the inconsistent of the response characteristics of each pixel in infrared detector results in the inhomogeneous of the thermal images. Before the infrared thermal image can be used for tracking coal mining machine, it needs to be denoised. In this work, the original infrared thermal images were denoised by the proposed method and other methods, and the denoised images are shown in Figure 21.  
As the images were denoised, the processed images would have less information than before. Therefore, information entropy was used to represent the amount of information contained in the image: the greater the value of information entropy, the more information the image contains. As the noise in the images was wiped by denoising methods, the denoised images were smoother than before. The information entropy (\(le\)) and smoothness (\(Smo\)) were introduced to evaluate the images in Figure 21, which could be calculated as  
\[le=-\sum_{k=1}^{K}H_{k}\log H_{k}. \tag{26}\]  
\[Smo=\frac{1}{M\times N}\sum_{i=1}^{m}\sum_{j=1}^{n}S_{best}(i,j) \tag{27}\]  
where \(K\) was the sum of gray scales in an image, \(H_{k}\) is the proportion of the pixels of gray value \(k\) in an image, and \(S_{best}(i,j)\) was the average gray value of the pixels in the best active window. The information entropy and smoothness of original images and processed images are plotted in Figure 22. As shown in Figure 22, we could find that the information entropies of images processed by different methods are almost the same. It showed the denoised images also had good quality and the major information of images was retained. In Figures 21 and 23, the images processed by proposed method had best smoothness. The smoothness of the images processed by IFOA-DTCWT-BF was 24.07, 24.28 and 23.41, which could not be acquired by other methods. It was beneficial for the following work of cutting unit tracking, as there were less unnecessary textures. The analysis results indicated that the images denoised by IFOA-DTCW-BF were better than other methods, which had better image textures and quality. Figure 20: Original image form the surface experiment. (**A**) Image form surface mining engineering; (**B**) Cutting unit of mining machine; (**C**) Infrared thermal image of cutting unit. Figure 21: Original images and denoised images using different methods.