## 3 Layered Processing  
The low-frequency information layer contains the background information of the image, which has the disadvantages of producing low contrast and fuzzy images, and needs to be processed for contrast enhancement. After the wavelet transform image is produced, the noise information is mainly contained in the high-frequency information layer. The low-frequency information layer of the null-domain method of enhancement  
Figure 5: Three-layer experimental results of the high-frequency information layer: (**a**) First horizontal high-frequency information layer, (**b**) First vertical high-frequency information layer, (**c**) First diagonal high-frequency information layer, (**d**) Second horizontal high-frequency information layer, (**e**) Second vertical high-frequency information layer, (**f**) Second diagonal high-frequency information layer, (**g**) Third horizontal high-frequency information layer, (**h**) Third vertical high-frequency information layer, (i) Third diagonal high-frequency information layer. can be effectively avoided to enhance the contrast of the image and reduce the noise interference, linear gain, \(\gamma\) transform and histogram equalisation algorithms, as well as other null-domain methods, to enhance the contrast to good effect. Histogram algorithms are currently the most widely used. In this study, the CLAHE algorithm is selected to process the low-frequency information layer image obtained by wavelet transform. The core idea of the histogram equalisation algorithm (HE algorithm) is to re-count the grey values of the infrared image and distribute the intervals where the grey levels of the infrared image are concentrated uniformly over the whole range of grey levels using Equation (3) [16]:  
\[S_{\mathbf{k}}=T(r_{k})=(L-1)\sum_{j=0}^{k}p_{r}(r_{j})=\frac{(L-1)}{MN}\sum_{j =0}^{k}n_{j}(k=0,1,\cdots,L-1) \tag{3}\]  
where \(r_{j}\) denotes the grey value of point \(j\), \(p_{r}\) represents the probability of the grey value of point \(r\), \(k\) denotes the first grey value, \(L\) denotes the maximum grey value, and \(MN\) denotes the size of the image. Through processing the above mapping formula, the global contrast of the image can be increased, and the effect is especially obvious for the images with concentrated grey-scale intervals. In addition, the HE algorithm does not need to set other parameters and can be run separately as a self-contained system, which has the advantages of simplicity and efficiency. This algorithm is widely used in medical imaging and other images. However, in practical applications, due to the differences between various parts of the image, the global histogram equalisation of the whole image is not good, and the adaptive histogram equalisation algorithm (adaptive HE (AHE)) solves this problem. The AHE algorithm is improved on the basis of the HE algorithm in the following way, as shown in Figure 6. First, the whole image is divided into many sub-blocks. Then, each sub-block is respectively processed by histogram equalisation, and the distribution function of each sub-block is statistically mapped, which can improve the local details of the image data. However, due to the chunking process, the whole image will present the block effect at the sub-block connection and the connection misalignment problem. This problem can be solved by bilinear interpolation, the principle of which is shown in Figure 6 [16]. The main idea of bilinear interpolation is to perform one linear interpolation in the \(x,y\) directions, separately; calculate the values in both directions; and compute the pixel values of \(o\) points using the following bilinear interpolation (Equation (4)). The bilinear method takes surrounding pixels with different weights (the closer distance, the higher weight) into consideration for computing the interpolated pixels, while the nearest neighbour method only can consider the pixels that in the closest distance for interpolation. Thus, bilinear  
Figure 6: AHE bilinear interpolation diagram.