# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 3 Results and Discussion
### Comparison with Other Image Dehazing Methods
#### 3.3.2 Qualitative Evaluations
Although DehazeFormer, Trinity-Net, and DCRD-Net achieve good dehazing effects, the texture details in the images produced by these methods do not match the ground truth images as closely. In contrast, both the teacher model SCSNet and the proposed DVKT framework effectively preserve the overall structure, local details, and color accuracy of the ground truth images, resulting in visually pleasing and realistic dehazed images. Figure 4: Visual comparisons of dehazing performance across various methods on the SateHaze1k-thick 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 5 showcases the visual results of various methods on the RS-Haze test set. While several methods demonstrate varying degrees of success, some exhibit notable shortcomings. For instance, SLP- and DCP-restored images often suffer from color distortion and residual haze. AOD-Net, DehazeFormer, Trinity-Net, DCRD-Net, and FCTF-Net frequently over-dehaze, leading to a loss of fine details. Light-DehazeNet, on the other hand, struggles with color distortion and detail preservation. In contrast, both SCSNet and the proposed DVKT framework effectively restore texture details and enhance image quality, aligning more closely with the ground truth. Figure 6 presents the visual comparison of various methods on the RSID test set. Obviously, SLP and DCP-restored images suffer from severe color distortion. The images restored by AOD-Net and Light-DehazeNet suffer from a significant loss of texture details and exhibit notable color distortions. DehazeFormer, Trinity-Net, DCRD-Net, and FCTF-Net can avoid color distortions to some extent, but the generated images still have some haze that cannot be removed. In contrast, both SCSNet and the proposed DVKT framework effectively preserve color fidelity and texture details, producing visually superior results. Figure 5: Visual comparisons of dehazing performance across various methods on the RS-Haze 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). To further demonstrate the generalization capability of the proposed method, we conducted performance verification on the RRSD300 dataset. Since the RRSD300 dataset only contains real-world hazy images without corresponding ground truth images, all models are trained on SateHaze1k dataset and trained on the RRSD300 dataset. The visualization results are shown in Figure 7. It can be observed that the dehazing results obtained by DCP and SLP exhibit significant color distortion. AOD-Net and DCRD-Net introduce some artifacts, resulting in poorer dehazing outcomes. Light-DehazeNet's dehazing results lose considerable detail information. Although FCTF-Net is a dehazing method tailored for remote-sensing images, it also suffers from color distortion. In contrast, the adopted teacher network SCSNet and the proposed DVKT framework achieve better dehazing results, indicating the good generalization and practicality of the proposed DVKT framework method. Figure 6: Visual comparisons of dehazing performance across various methods on the RSID 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).