## 1 Introduction  
In recent years, with the increased demands of photography, transportation, military, aerospace and other fields, image dehazing has gradually become a popular area of research in image processing. Researchers generally deal with haze from the perspectives of image enhancement and image restoration. The former focuses on enhancing low-level features of images, such as contrast, sharpness, edges, low-light stretch, histogram equalization, and homomorphic filtering [1; 2; 3; 4; 5]. In the research on the physical degradation model of foggy images, the latter method enriches image information lost in the fogging process by optimally estimating the haze-free images [6; 7; 8]. However, the estimation of parameters in the degradation model is a challenging task, as it is an ill-conditioned problem based on the unique given condition of one hazy image. Therefore, some image restoration methods require additional information or assumptions [9; 10; 11]. [PERSON] [12] used a haziness flag to measure the degree of haziness, which obtains the adaptive initial transmission value by establishing the relationship between the image contrast and the haziness flag. The method has superior haze removal and color balancing capabilities for images with different haze densities. In addition, [PERSON] [13] estimated the irradiance of the scene and deduced the transmittance image based on the assumption that the transmittance of the light and shadow of the object surface in the scene were locally irrelevant. To deal with outdoor images in sand-dust environments, [PERSON] [14] used successive color balance with a coincident chromatic histogram to adjust the pixels of each color component based on the statistical characteristics of the green component. [PERSON] et al. [15] improved the dark channel prior theory and applied the contrast-limited adaptive histogram equalization (CLAHE) method to enhance the optimized transmittance image. This method has made significant improvements over the original DCP method and can be applied to the defogging of infrared dense fog images. [PERSON] [16] presented the reason for underwater image degradation. The state-of-the-art intelligence algorithms, such as deep learning in underwater image dehazing and restoration, were surveyed, demonstrating the performance of underwater image dehazing and color restoration with different methods. The paper introduced an underwater image color evaluation metric and provided an overview of the major underwater image applications. This provides great convenience for follow-up research. [PERSON] et al. [17] designed a fully end-to-end dehazing network for single image dehazing named the dense residual and dilated dehazing network (DRDDN). A dilated, densely connected block was designed to fully exploit multi-scale features through an adaptive learning process. The deep residual was used to propagate the low-level features to high-level layers. [PERSON] et al. [18] used an adversarial game between a pair of neural networks to accomplish end-to-end photorealistic dehazing. The generator learned to simultaneously restore the haze-free image and capture the non-uniformity of haze to avoid uniform contrast enhancement. A task-driven training strategy was proposed to optimize the object detection of dehazed images without updating the parameters of the object detector. [PERSON] et al. [19] proposed an end-to-end feature fusion attention network (FFA-Net) to directly restore the haze-free image. The network was composed of three parts: (1) a novel feature attention (FA) module, (2) a basic block consisting of local residual learning and feature attention, and (3) an attention-based different-level feature fusion structure. The feature weights were adaptively learned from the FA module, giving more weight to important features. The experimental results demonstrated strong progress in both indoor and outdoor defogging fields. All of the above deep learning-based defogging methods have good defogging effects. However, they also have certain drawbacks.