## 1 Introduction  
For digital images, the dynamic range is the ratio between the maximum possible grey value and the minimum grey value of an image pixel, which can also be expressed as the difference between the two [1]. In real imaging, the background information, such as the sea and sky, often has a large temperature difference, while the temperature difference of the object to be imaged is relatively small. Thus, the imaging system provides poor detail information. In order to improve the spatial and temperature resolution of the infrared system, the modern infrared imaging system is commonly applied to large-scale infrared focal plane arrays and high-level analogue-to-digital converters, which greatly improves the dynamic range of infrared images [1]. Compared with traditional infrared images, high dynamic infrared images have a wider brightness range and richer detail information, so they are important in target detection, identification, tracking and other critical tasks; however, high dynamic infrared images also suffer from high noise levels, low contrast, and other problems, which seriously affect the quality of the image and the accuracy of the subsequent processing. In the field of infrared image processing, how to effectively highlight the hierarchy of images, improve the image contrast, and enhance detail has become a research task of great interest. The importance of this work lies not only in the improvement in the quality of infrared images, but also in the fact that it achieves a richer presentation of image information under the conditions of a high signal-to-noise ratio with moderate computational speed. Therefore, the aim of this study is to investigate how to process high dynamic infrared images in order to effectively enhance the layering of infrared images and further increase the detail information in the images, thus improving their applicability and usefulness. Image enhancement refers to emphasising or sharpening certain features of an image, such as edges, contours or contrast. The traditional methods currently applied to image processing can be divided into two main categories: spatial domain enhancement [2; 3; 4; 5; 6] and frequency domain enhancement [7; 8; 9; 10]. Spatial domain enhancement includes methods such as automatic gain conversion (AGC), gamma transformation (\(\gamma\)-transformation) and histogram equalisation (HE), which achieve the effect of image enhancement by directly changing the pixel values of the image. Spatial domain enhancement has the characteristics of a simple algorithm and fast processing speed, which can be better applied and popularised. However, there are also problems, such as image noise amplification due to the overall changes in the image, and the enhancement effect is not obvious. In frequency domain enhancement, the spatial domain is transformed into the frequency domain. By modifying the magnitude and other information of the image, the relevant features in the image are enhanced, which enhances the recognition ability of the image and simultaneously effectively reduces the noise of the image. From the first application of Fourier transform techniques to image processing by [PERSON] and [PERSON] in 1966 [11] to the application of wavelet transform processing in image processing by [PERSON] in 1988 [12], the frequency domain enhancement field has come a long way. Since the development of image processing, a single method can no longer meet the needs of image processing, and image enhancement algorithms combining advantages are more meaningful for research [13; 14]. In this study, a combined time-frequency domain approach is used to process the image. The image is first decomposed using a discrete steady 2D wavelet transform (discrete stationary 2D wavelet transform (SWT2)) [15]. Secondly, the low-frequency subbands decomposed by SWT2 are processed using contrast-limited adaptive histogram equalisation (CLAHE) [16], which can effectively enhance the contrast of the image.