# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 4 Experiments and Discussions  
### Experimental Settings  
In this section, we provide details about the experimental settings, including the datasets, training procedures, and baselines. **Datasets. ** We evaluate our proposed method on both real-world and synthetic hazy image datasets. For real-world hazy images, we use the aerial RS hazy images from [PERSON] et al. 's work [38] and Flickr for qualitative analysis. For quantitative evaluation, we synthesize uniform and non-uniform RS hazy images. We select 90 non-uniform RS hazy images with different haze densities from the SateHaze1k dataset [39]. To generate the uniform RS hazy dataset, we use images from the DeepGlobe road extraction dataset [40] and apply the haze imaging model in Equation (1), with the atmospheric light, \(A\), and transmission map, \(t\), uniformly defined between \([0.6,1]\) and \([0.2,0.7]\), respectively. Eventually, we have 90 images as the uniform RS hazy datasets, which are also chosen for the high-level vision task analysis in Section 4.5. **Training details. ** We train the proposed model using the PyTorch toolbox on the Ubuntu 20.04 LTS operating system. We use the Adam optimizer with a fixed learning rate of 0.0001. As a zero-shot dehazing method, we train the model for each image for approximately 500 iterations to obtain the final result. We empirically set the balancing parameters \(\alpha\) and \(\beta\) in Equation (11) to \(10^{-5}\) and \(10^{-6}\), respectively. The training is performed on a PC with an i7-9700F processor @3.00 GHz, 24 GB RAM, and a NVIDIA RTX 3080 GPU. We present the training details of each iteration in Algorithm 1. **Baselines. ** In order to verify the performance of our proposed method, we compared it to various state-of-the-art (SOTA) dehazing methods, including traditional, learning-based, and zero-shot dehazing methods. Table 1 presents a classification of each method according to its dehazing type and a brief explanation for better comprehension. The traditional dehazing methods encompass a prior-based method (\(DCP\)[8]) and enhancement-based methods (CEEF [6] and FADE [41]). The learning-based methods include supervised learning-based methods (DehazeFlow [42], SRKTDN [43], and DWGAN [44]), a semi supervised learning-based method (RefineDNet [17]), and an unsupervised learning-based method (USIDNet [45]). The zero-shot dehazing methods evaluated are ZID [14], DDIP [35], YOLY [13], ZIR [16], and our proposed method. As there is no ground truth image available for a real-world RS hazy image, we use the result obtained from the dehazing tool of Photoshop 2021 as a reference. To assure a fair comparison, all methods were tested using MATLAB or PyCharm in the same software and hardware environment. For learning-based methods, we chose the default configurations from the literature and used their pre-trained models for a more accurate generalizability comparison. For zero-shot dehazing techniques, the models were trained with their default parameters. For ZID [14], DDIP [35], YOLY [13], and ZIR [16], the training iterations are 500, 4000, 500, and 10,000, respectively. We quantitatively analyzed the dehazing performance using three metrics, namely, peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and learned perceptual image patch similarity (LPIPS) [46]. The PSNR and SSIM metrics are commonly used to evaluate image quality, while LPIPS is a metric based on a deep neural network for assessing the perceptual similarity between two images.