## 3 Layered Processing
method can achieve superior performance when compared to the nearest neighbour method. \[S_{O}=(1-x)(1-y)g_{A}(r_{o})+x(1-y)g_{B}(r_{o})+(1-x)yg_{C}(r_{o})+xyg_{D}(r_{o}) \tag{4}\]  
where \(g_{A},g_{B},g_{C},g_{D}\) denote the mapping function of each sub-block; \(r_{o}\) denotes the pixel value of point \(o\); and \(x,y\) denote the horizontal and vertical displacements, respectively. In Figure 6, A, B, C, and D denote the pixel values of the point, separately, and the o point is the pixel of the requested point. The specific application in the image is shown in Figure 7:  
1. Divide the image into several rectangular blocks. For each rectangular block sub-map, calculate its grey-scale histogram and the corresponding transformation function (cumulative histogram), separately. 2. Process the pixels in the original image, according to their distribution, into three cases: * The pixels in the red area are grey mapped according to the transform function of the sub-map in which they are located; * The pixels in the green area are linearly interpolated by the transform function of two neighbouring subgraphs; * The pixels in the blue region are bilinearly interpolated according to the transform function of the four neighbouring subgraphs. Finally, the CLAHE algorithm is formed by limiting the pixels with excessive image frequency and adding the intercepted pixel values uniformly over the entire range, as shown in Figure 8.  
Through the application of three histogram equalisation algorithms to the image low-frequency information layer, a comparison of the experimental results can be obtained. The three histogram processing steps of the low-frequency information layer image contrast enhancement have significant effects. The HE algorithm's experimental results are shown in Figure 9a. There are excessive enhancement problems: the image has been lost in part of the information, the wavelet transformed low-frequency information layer image processing is poor, and the results are not applicable to the combination of this algorithm. The experimental results of the AHE algorithm are shown in Figure 9b, which shows that there is no over-enhancement problem for the image processing of the low-frequency information layer. However, the image as a whole is too bright, as shown in the yellow box in Figure 9b. The image details are poorly displayed, and the contrast is increased. However, the effect is not optimal. The CLAHE algorithm experim  
Figure 8: Image contrast limit. Figure 7: Bilinear interpolation image processing diagram. in Figure 9c; the image retains the original low-frequency information layer's background, as shown in the image in the yellow box in Figure 9c. The contrast enhancement also has a significant effect, and the visual effect is significant, making the image suitable for combining with the wavelet transform for the processing of high dynamic infrared images. The high-frequency information layer contains the detail and contour information of the image, as well as the noise information of the image, which first needs to be denoised. In addition, the effective information should be enhanced for subsequent image applications [22; 23; 24]. Traditional image denoising, such as median filtering, mean filtering, etc., assume that the signal and noise are in different frequency bands. However, the noise is distributed over the entire frequency axis, so there are certain limitations. By decomposing the image, the wavelet transform mainly focuses the high-frequency information and noise information on the high-frequency information layer and thresholds the high-frequency information layer for denoising. Coefficient gain is applied to the denoised high-frequency information layer image to enhance the detail information and contour edges of the image and improve the visualisation ability of the image. The coefficient gain of the image is selected using adaptive gamma, which greatly improves the applicability of the method.