# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 3 Results and Discussion
### Experimental Details and Evaluation Criteria  
(1) Experimental Details: The proposed DVKT framework is implemented with PyTorch 1.13, and the experiments are conducted on a PC with NVIDIA RTX 3090 GPU (NVIDIA, Santa Clara, CA, USA). To augment the training dataset, random rotations of 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), as well as horizontal flipping are applied. The inputs to the DVKT framework are with the spatial size of 256 \(\times\) 256. For the training process, a batch size of 32 was used, and the number of epochs was set to 20. The DVKT framework was optimized using the Adam optimizer. The initial learning rate configured at 1.0 \(\times\) 10\({}^{-4}\), with a cosine annealing strategy employed to progressively reduce it to 0. To address the high cost associated with tuning weight coefficients, a multi-task learning approach based on equal variance and uncertainty [43] was implemented to determine the optimal weight coefficients \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\). Empirically, the hyperparameter n in 1/n-SCSNet was set to 5.  
(2) Evaluation Criteria: To assess the performance of the proposed DVKT framework, the Peak Signal-to-Noise Ratio (PSNR), the Structural Similarity Index Measure (SSIM), and the Learned Perceptual Image Patch Similarity (LPIPS) are used as quantitative metrics. Higher PSNR and SSIM or lower LPIPS values signify superior quality in the restored images. They can be expressed as follows:  
\[\text{PSNR}=20\cdot\log\left(\frac{MAX_{I}}{MSE}\right), \tag{11}\]  
\[\text{SSIM}\left(\mathbf{I}_{\text{gt}},\mathbf{O}_{\text{s}}\right)=\frac{ \left(2\mu_{gt}\mu_{s}{+}c_{1}\right)\left(2\sigma_{gt}{+}c_{2}\right)}{\left( \mu_{gt}{}^{2}+\mu_{s}{}^{2}+c_{1}\right)\left(\sigma_{gt}{}^{2}+\sigma_{s}{} ^{2}+c_{2}\right)}, \tag{12}\]  
\[\text{LPIPS}\left(\mathbf{I}_{\text{gt}},\mathbf{O}_{\text{s}}\right)=\sum_{i }\frac{1}{H_{i}W_{i}}\sum_{h,v}\left\|w_{i}\cdot\left(h_{hw}^{i}-O_{hw}^{i} \right)\right\|_{2}^{2}, \tag{13}\]where \(MAX_{I}\) denotes the maximum pixel value of \(\mathbf{I}_{\text{gt}}\), and MSE represents the mean squared error between the corresponding pixels of \(\mathbf{I}_{\text{gt}}\) and the reconstructed haze-free image \(\mathbf{O}_{\text{s}}\). \(\mu_{gt}\) and \(\mu_{s}\) are the means of \(\mathbf{I}_{\text{gt}}\) and \(\mathbf{O}_{\text{s}}\), respectively. \(\sigma_{gt}\) and \(\sigma_{s}\) mean the standard deviations of \(\mathbf{I}_{\text{gt}}\) and \(\mathbf{O}_{\text{s}}\), respectively. \(\sigma_{gt}\) is the covariance between \(\mathbf{I}_{\text{gt}}\) and \(\mathbf{O}_{\text{s}}\). \(w_{i}\) means the weight for each image channel. \(c_{1}\) and \(c_{2}\) are constants introduced to maintain stability in the computation.  
Furthermore, to evaluate the model size and computational cost, Params and floating-point operations (FLOPs) are adopted:  
\[\text{Params}=(K_{h}\cdot K_{w}\cdot C_{in}+1)\cdot C_{out}, \tag{14}\]  
\[\text{FLOPS}=2 HW(K_{h}\cdot K_{w}\cdot C_{in}+1)\cdot C_{out}, \tag{15}\]  
where \(C_{in}\) represents the channel number for the input feature, \(K_{h}\) and \(K_{w}\) denote the kernel size, and \(C_{out}\) signifies the channel number for the output feature. \(H\) and \(W\) correspond to the height and width of the feature map. In this study, the units of Params and FLOPs are millions (M) and billions (G), respectively. Furthermore, the model's inference time, measured in seconds, is determined by averaging the processing times of 100 haze-degraded images. During the experiments, all comparative methods utilize input images with a spatial dimension of 256 \(\times\) 256.