# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 2 Related Work  
### Traditional Dehazing Methods  
Due to atmospheric haze, RS images often suffer from low contrast and poor visibility, and traditional image enhancement techniques have been applied to improve their quality. In the spatial domain, [PERSON] et al. [18] observed a linear correlation between the minimum channels of hazy and clear images and proposed a fast linear transformation for haze removal. Similarly, [PERSON] et al. [19] proposed a pixel-level linear intensity transformation to enhance the visibility of hazy satellite images. However, spatial enhancement cannot effectively remove haze because it only increases the image's contrast. In the frequency domain, many dehazing methods perform dehazing and detail enhancement separately in low-frequency and high-frequency bands, respectively. For instance, [PERSON] et al. [7] decomposed the input hazy image into multi-scale representations using wavelet transform and applied a radiance transformation function to the wavelet coefficients of each scale to remove haze. Likewise, [PERSON] et al. [20] proposed a model-assisted hazy image enhancement algorithm by extracting multi-band features using an image decomposition module. [PERSON] et al. [21] separated details from RS hazy images using a multiscale guided filter, and implemented a nonlinear mapping for detail enhancement. Although the majority of haze exists in the low-frequency domain, dehazing in this domain alone is insufficient to remove haze completely.  
Prior-based methods have been investigated to solve the dehazing problem by exploiting the statistics or characteristics of a large number of hazy and clear images. Many classic hazy image priors, such as \(DCP\)[8], CAP [9], and haze-lines [10], have been shown to be effective. However, these priors are incapable of handling complex real-world ambiguous situations and may fail in certain circumstances. Therefore, new priors are constantly being proposed. For example, [PERSON] et al. [22] used the gradient profile prior (GPP) to estimate the transmission map and enhance image details, but the GPP may result in noise amplification. [PERSON] et al. [23] proposed a local patch-wise minimal and maximal values prior for RS image dehazing. Although this algorithm is efficient and effective, the brightness of the dehazed results is darker. [PERSON] et al. [24] proposed a defogging algorithm that combines both the traditional dark channel prior method and the fusion-based method. However, slight color distortion can be found in some dehazed images. [PERSON] et al. [25] proposed an RS image enhancement approach by incorporating the \(DCP\) and linear transformation, combining the advantages of image enhancement and prior dehazing. However, the empirically predefined parameters may have significant impacts on dehazing performance.  
As detailed in the above discussions, the enhancement-based methods can better retain image details, while the prior-based methods are better at removing haze. However, the enhancement-based methods simply enhance the hazy image's contrast without considering the haze imaging model, resulting in a poor dehazing effect. The hazy image priors are not sufficient enough for the complicated real-world hazy conditions, which may bring undesired artifacts in some scenarios.