# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 3 Results and Discussion
### Comparison with Other Image Dehazing Methods
#### 3.3.2 Qualitative Evaluations  
Figure 2 showcases the qualitative results of various methods applied to the SateHaze1k-thin dataset. The dehazed images from DCP and FCTF-Net display noticeable color distortions. A significant amount of haze persists in the images processed by AOD-Net and Light-DehazeNet. Methods such as FCTF-Net and Light-DehazeNet achieve some level of dehazing; however, compared to the ground truth images, there is still a minor presence of haze in the restored images. Similarly, DehazeFormer, SLP, Trinity-Net, and DCRD-Net manage to remove some haze, but the overall restoration still leaves a slight haze residue compared to the ground truth. The dehazed images produced by the teacher model SCSNet and our DVKT framework are more closely aligned with the ground truth images, achieving a higher level of detail recovery. It is important to note that our DVKT framework boasts a smaller model size and higher inference efficiency. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Params** & **FLOPS** & **Time** & **PSNR** & **SSIM** & **LPIPS** \\ \hline DCP [11] & — & — & 0.2604 & 18.5833 & 0.8344 & 0.1602 \\ AOD-Net [46] & 0.01 & 0.15 & 0.0452 & 17.4859 & 0.6332 & 0.2498 \\ Light-DehazeNet [48] & 0.03 & 1.97 & 0.0571 & 17.8132 & 0.8352 & 0.1199 \\ FFA-Net [23] & 4.45 & 287.53 & 0.4465 & 21.2873 & 0.8663 & 0.0898 \\ Restormer [49] & 26.13 & 141.17 & 0.3157 & 20.7892 & 0.8379 & 0.0749 \\ DehazeFormer [28] & 9.68 & 89.85 & 0.2438 & 22.0066 & 0.8659 & 0.0599 \\ Trinity-Net [19] & 9.82 & 90.17 & 0.2615 & 17.6508 & 0.7864 & 0.1940 \\ DCRD-Net [50] & 0.04 & 14.92 & 0.1827 & 21.7468 & 0.8812 & 0.0549 \\ FCTF-Net [29] & 0.16 & 10.04 & 0.1574 & 19.5883 & 0.8434 & 0.1098 \\ SCSNet (Teacher Network) [37] & 1.53 & 10.79 & 0.1713 & 25.1759 & 0.9223 & 0.0419 \\
**DVKT** & **0.06** & **0.44** & **0.0225** & **23.9271** & **0.9105** & **0.0509** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of inference efficiency on the Satehaze1k dataset. Figure 2: Visual comparisons of dehazing performance across various methods on the SateHaze1k-thin dataset. The red box emphasizes key areas for comparison. (**a**) Hazy Image; (**b**) GroundTruth; (**c**) DCP [11]; (**d**) AOD-Net [46]; (**e**) Light-DehazeNet [48]; (**f**) DehazeFormer [28]; (**g**) SLP [45]; (**h**) Trinity-Net [19]; (**i**) DCRD-Net [50]; (**j**) FCTF-Net [29]; (**k**) SCSNet (Teacher Network) [37]; (**l**) DVKT (ours). Figure 3 displays the visual results of multiple methods on the SateHaze1k-moderate test set. Under moderate haze conditions, crucial information in remote sensing images can become obscured. Apart from SCSNet and the proposed DVKT framework, other dehazing methods result in images with some residual haze. DCP, AOD-Net, SLP, and Light-DehazeNet show significant color distortion. While DCRD-Net and Trinity-Net achieve a certain level of dehazing, there remain noticeable differences when compared to the ground truth image. In contrast, both the teacher model SCSNet and the proposed DVKT framework produce visually more pleasing results, with enhanced color fidelity and better preservation of texture details. Figure 3: QVisual comparisons of dehazing performance across various methods on the SateHaze1k-moderate dataset. The red box emphasizes key areas for comparison. (**a**) Hazy Image; (**b**) GroundTruth; (**c**) DCP [11]; (**d**) AOD-Net [46]; (**e**) Light-DehazeNet [48]; (**f**) DehazeFormer [28]; (**g**) SLP [45]; (**h**) Trinity-Net [19]; (**i**) DCRD-Net [50]; (**j**) FCTF-Net [29]; (**k**) SCSNet (Teacher Network) [37]; (**l**) DVKT (ours). Figure 4 illustrates the qualitative results of different methods when applied to the SateHaze1k-thick test set. This particular set includes remote-sensing images heavily affected by dense haze, causing a significant loss of texture detail information. Methods such as DCP, AOD-Net, Light-DehazeNet, SLP, and FCTF-Net show evident color distortions.