## 2 Related Work
### Image Dehazing  
Existing image-dehazing algorithms can be divided into three categories: image-enhancement-based dehazing algorithms, physical model-based dehazing algorithms, and deep-learning-based dehazing algorithms. Although these three types of algorithms have achieved certain results, they still each have certain limitations, resulting in poor dehazing results. Image enhancement-based algorithms achieve dehazing by enhancing the contrast and saturation of the image, such as histogram equalization [32], Retinex [33], and wavelet transformation [34]. These methods are simple in principle and low in computational complexity; however, because they do not consider the degraded physical factors of haze, they easily lead to problems, such as excessive local area enhancement, color distortion, and halo artifacts in the haze-free image. Physical model-based dehazing algorithms are mainly based on the atmospheric scattering model; they obtain some mapping relationships through the observation and summary of a large number of haze images and haze-free images, and then use the reverse process of the formation process of haze images to restore clear images. The atmospheric scattering model was developed from the atmospheric scattering theory[5]; the model was formally proposed by [PERSON] and then further summarized by [PERSON] et al. [5; 35]. The model considers incident light attenuation and scattering medium effects as the main factors causing the degradation of image quality; therefore, only the atmospheric light coefficient and transmittance need to be estimated to calculate the clear image. [PERSON] et al. [36] proposed the Dark Channel Prior (DCP) theory based on a large number of statistical studies of haze-free images. According to this theory, atmospheric scattering model parameters can be estimated to obtain a haze-free image. This method is simple, effective, and widely used, but it is not effective in high-brightness regions, such as the sky. [PERSON] et al. [37] proposed Color Attenuation Prior (CAP) based on statistical experiments, thus establishing a model for luminance, saturation, and depth, and the model parameters were determined by supervised learning methods to recover the haze-free image. [PERSON] et al. [38] proposed non-local dehazing based on statistical experiments, where RGB values in a haze-free image are clustered into different clusters; under the influence of haze, the clusters in the haze-free image become \"haze lines\" with atmospheric light as the origin, and the transmittance is estimated by the distance from the image point to the origin point in the haze lines. Although the physical model-based image-dehazing algorithm takes into account the physical factors that affect the dehazing effect, the physical model is not accurate due to the complex physical causes, which affect the dehazing effect. Deep-learning-based dehazing algorithms learn the mapping between haze and haze-free images from the training data by building a neural network model, thus overcoming the problem of the difficult physical modeling of haze and obtaining better results. Two main deep-learning-based dehazing algorithms are widely used. One is to estimate the parameters in the atmospheric scattering model by constructing a neural network model and then use the estimated parameters to achieve image dehazing. [PERSON] et al. [13] introduced convolutional neural networks to the image-dehazing task; they proposed the DehazeNet model to learn the mapping between the haze image and transmittance, and then estimate the parameters in the atmospheric scattering model in order to obtain the haze-free image. This method inputs the haze image, outputs the transmittance estimation map, and then recovers the haze-free image through the atmospheric scattering model while using a new nonlinear activation function to improve the quality of the recovered haze-free image. On this basis, [PERSON] et al.