## 2 Related Work  
Since the introduction of image dehazing, various algorithms have been proposed. According to their processing methods, these algorithms can be roughly divided into image enhancement-based methods, physical model-based methods, and deep learning-based methods. Image enhancement-based methods can improve the visual quality of images by enhancing their contrast. [PERSON] et al. [6] proposed a foggy image enhancement algorithm based on bilinear interpolation dynamic histogram equalization, which divides the image histogram into several sub-histograms and assigns specific gray level ranges to these sub-histograms, thus ensuring that no serious edge effects are generated while enhancing the image. [PERSON] [7] proposed a ship video surveillance image dehazing algorithm based on adaptive histogram equalization. The algorithm converts the hazy image into a histogram and equalizes the gray levels of the histogram. [PERSON] et al. [8] proposed using the modified Butterworth filter as the transfer function for homomorphic filtering. Considering the different propagation characteristics of signal and noise on different wavelet transform scales, [PERSON] et al. [9] proposed an adaptive threshold estimation method that alters the change of decomposition scale. [PERSON] et al. [10] proposed using the improved wavelet function combined with the dark channel algorithm for multiple fusion to dehaze the source image. [PERSON] et al. [11] proposed transforming the image to HSI color space and using the improved Retinex algorithm to enhance the I component to achieve haze removal. [PERSON] et al. [12] proposed using the multiscale retinex technique to eliminate the uneven information of ambient atmospheric light values and only retain the reflection of the object's surface. The grayscale range of images is stretched in these methods, significantly improving their contrast. They are simple and easy to use, with obvious visual effects. However, the fundamental reasons for image quality degradation were not considered in this type of method, leading to the loss of image detail information or over-enhancement during dehazing, resulting in poor robustness. Physical model-based methods can analyze image information according to the mathematical model of foggy imaging [13], and they can use prior knowledge to obtain the dehazed image. As shown in Figure 1, the model is mainly based on the scattering and absorption of light in atmospheric media and simulates the image degradation phenomenon that occurs under complex atmospheric conditions. Therefore, researchers have proposed several methods to estimate the correlation coefficient for calculating the defogged image. Among these, the dark channel prior method proposed by [PERSON] et al. [14] is one of the most common algorithms. Due to the large sky areas and high white light components, it is easy to obtain higher values in all three channels using this method. Therefore, researchers have proposed a series of improved algorithms based on the dark channel prior method. [PERSON] et al. [15] proposed segmenting the image using the four-division method to select the best area for the estimation of atmospheric light value, aiming to address the problem of color spots in the sky area during the processing of the original dark channel prior theory. [PERSON] et al. [16] proposed segmenting the sky region based on the gray feature value to better calculate the atmospheric light value. In the bright channel prior theory, it is assumed that at least one color channel pixel in certain target areas has a reflectance of 100%, allowing for better differentiation of images in large sky backgrounds using this theory. [PERSON] et al. [17] proposed using the L channel of the Lab color space as a guide map, predicting the atmospheric light value through guided filtering, and then estimating the transmittance by using the bright channel prior theory. [PERSON] et al.