# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 1 Introduction  
Remote sensing images (RSIs) now have widespread uses in many computer vision (CV) applications [1], including land cover classifications [2], road extractions [3], semantic segmentation [4], target detection [5], and others. However, due to interferences from haze or cloud, the acquired RSIs often suffer from blurring and haziness, which significantly limits the potential of high-level CV applications. Therefore, image dehazing has become an essential preprocessing operation for enhancing the visual quality of RSIs. Traditionally, researchers have attempted to directly enhance the quality of hazy images in either the spatial or frequency domain, using what are known as enhancement-based methods. In the spatial domain, [PERSON] et al. [6] improved the local visibility and global contrast of hazy images using gamma correction and adaptive histogram equalization, respectively. Their proposed dehazing method offers the advantages of good detail preservation and less color distortion. Conversely, [PERSON] et al. [7] addressed the dehazing problemin the frequency domain. They decomposed hazy images into high-frequency and low-frequency bands using wavelet transformation, where the atmospheric light was estimated in the high-frequency sub-bands and the low-frequency sub-band was selected for haze removal. Although these enhancement-based methods significantly improve the contrast and detail of hazy images, they are unable to remove haze effectively without considering the underlying principles of haze generation. Additionally, the degree of enhancement is not adaptive, which can lead to over-enhancement in some cases. In contrast to simple image enhancement, some researchers have proposed hazy image priors to reverse the haze imaging model for dehazing, known as prior-based dehazing methods. Several hazy image priors have been proposed in the literature, including the dark channel prior (_DCP_) [8], color attenuation prior (CAP) [9], and haze-lines [10], etc. The _DCP_ observes that the intensity of the minimum value of the color channels of a hazy image tends to be very low in regions with low haze or fog, while haze-lines are based on the assumption that the haze in an image is uniformly distributed along a line of sight and that the attenuation of the haze is proportional to the distance from the camera. As prior-based methods fully consider the hazy image degradation mechanism, they are generally able to remove haze more thoroughly than enhancement-based methods in most cases. However, since most hazy image priors are based on statistical observations or assumptions, they are unable to handle complex real-world hazy scenarios, and may fail under certain circumstances. For example, the _DCP_ may produce undesired artifacts in the sky region, and the resulting dehazed image may have a dark color. In recent years, learning-based dehazing methods that use convolutional neural networks (CNNs) have become increasingly popular. These methods can be broadly categorized into two groups: end-to-end methods that directly recover the clean image from the hazy image, and model-based methods that use CNNs to learn the parameters of the haze imaging model. For end-to-end dehazing methods, [PERSON] et al. [11] designed a feature fusion attention network (FFA-Net) to achieve end-to-end dehazing. The FFA-Net incorporates a feature fusion module and an attention mechanism to capture both low-level and high-level features of hazy images. For model-based methods, [PERSON] et al. [12] proposed a patch map-based hybrid learning DehazeNet (PMHLD), which combines the strengths of learning-based and prior-based methods to estimate the transmission map of hazy images accurately. Although learning-based dehazing methods have demonstrated many advantages over traditional dehazing methods, they require large-scale datasets for model training, in either a supervised or an unsupervised manner, making data collection a time-consuming task.