# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 2 Related Work
### Learning-Based Dehazing Methods with Training Datasets  
Due to the rapid development of CNNs, learning-based dehazing methods have been extensively investigated and can be categorized into three groups based on learning strategy: supervised learning-based, unsupervised learning-based, and semi-supervised learning-based. The supervised learning-based dehazing methods use paired hazy/clean images for model training. For instance, [PERSON] et al. [26] integrated the dense residual network with the squeeze and excitation block as the basic module to design a dual-step cascaded dense residual network, which can extract multi-scale local and global features of the RS hazy image more effectively. Similarly, [PERSON] et al. [27] also selected the dense residual network to capture the RS image's fine details and used the corresponding saliency map generated from global contrast to guide network training. However, the proposed dense networks by [PERSON] et al. [26] and [PERSON] et al. [27] are too bulky to be implemented in real-time applications. Based on the encoder and decoder architecture, [PERSON] et al. [28] proposed a deep dehazing network for RS images with non-uniform haze and used the wavelet transform as an additional channel to retain image textures. Nevertheless, this non-uniform dehazing model will suffer from domain shift issues in processing uniform RS hazy images. As paired hazy/clean RS images in the real world are scarcely acquirable, most supervised learning-based dehazing methods use synthetic RS hazy datasets for training and validation. However, the trained model may not perform well in real-world image dehazing. Therefore, some unsupervised learning-based dehazing methods based on generative adversarial networks (GANs) have been proposed to improve the model's generalizability. [PERSON] et al. [29] proposed a novel Cycle-dehaze method for single-image dehazing, which utilizes a cycle consistency loss to train the generator and the discriminator in the CycleGAN architecture. The training procedure of Cycle-dehaze is time-consuming and the model is difficult to converge. [PERSON] et al. [30] proposed an enhanced attention-guided GAN, called Dehaze-AGGAN, for unpaired RS image dehazing. Although the Dehaze-AGGAN achieves better results than many unpaired dehazing methods, there is still space for performance improvement in comparison to paired dehazing models. [PERSON] et al. [31] presented a memory-oriented GAN (MO-GAN) that uses a memory module to store and retrieve the features of previously processed hazy images. The stored features are used to guide the generation of the haze-free image. Although unsupervised-based dehazing methods can address the lack of hazy and haze-free image pairs in RS applications, the training procedure of a GAN-based model is complex, and the model is difficult to converge. Therefore, semi-supervised learning-based methods are proposed, which use both synthetic paired data and real-world unpaired data for training. The paired data help the convergence of model training, while the unpaired data are used in the fine-tuning stage to improve the model's generalizability. [PERSON] et al. [32] propose a semi-supervised dehazing algorithm that uses both supervised loss and unsupervised loss for model training. However, this approach is less effective in extremely hazy conditions. [PERSON] et al. [33] designed an encoder-decoder network with a Gaussian process to better extract image features, and a physically guided process is conducted in the fine-tuning stage. [PERSON] et al. 's [33] method obtains good results in terms of both uniform and non-uniform dehazing, but many hyperparameters must be appropriately set for balance. Taking dehazing as an image translation task, [PERSON] et al. [34] design a weakly supervised framework to achieve both haze synthesis and haze removal in one network. However, the recovered haze-free image may have lost some detail.