# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 3 Results and Discussion
### Comparison with Other Image Dehazing Methods
#### 3.3.1 Quantitative Evaluations  
(1) Performance Comparison: The quantitative comparison results on the SateHaze1k dataset are summarized in Table 1. As indicated, traditional prior-driven dehazing methods such as DCP, CEP, MOF, and SLP exhibit lower PSNR and SSIM values, suggesting they struggle with the complexities of remote-sensing image dehazing, thus delivering subpar performance. Similarly, AOD-Net and Light-DehazeNet show rather mediocre results when applied to remote-sensing imagery, possibly due to the limitations of early lightweight neural networks in capturing the intricate mappings between hazy and clear images. Conversely, data-driven approaches like FFA-Net, Restormer, Trinity-Net, and DehazeFormer demonstrate higher PSNR and SSIM scores, highlighting their potential in enhancing remote-sensing image dehazing. Models such as DCRD-Net, FCTF-Net, and SCSNet, which are specifically crafted for remote-sensing dehazing tasks, are also included in the comparison. It can be observed that both DCRD-Net and SCSNet achieve very good dehazing performance, and SCSNet performs best among all comparison methods. In contrast, FCTF-Net performs generally, which may be due to its relatively complex network structure, while the data volume in the SateHaze1k dataset is limited, leading to model overfitting. It is worth noting that, except for the teacher network SCSNet used in this study, the proposed DVKT framework delivers a state-of-the-art dehazing performance. Additionally, our DVKT framework matches the dehazing performance of the teacher network, SCSNet, but with a reduced number of parameters and faster inference times. Table 2 illustrates the quantitative comparison results across the RS-Haze and RSID datasets. As indicated, the proposed method demonstrates notable advantages over other compared approaches. Specifically, aside from the teacher network SCSNet utilized in this study, the DVKT framework shows superior dehazing performance. On the RS-Haze dataset, it achieves PSNR and SSIM values of 31.4227 dB and 0.9135, respectively. For the RSID dataset, the PSNR and SSIM values are 24.1082 dB and 0.9426, respectively. (2) Efficiency Comparison: One of the core objectives of this work is to enhance the efficiency of RSID models. To substantiate the effectiveness of the proposed DVKT framework in dehazing tasks, an exhaustive comparison of inference efficiency is conducted with the existing RSID models. Table 3 details the model sizes and inference efficiencies of various models. Concretely, it lists the model parameters (Params), FLOPs, and the average time required to process 100 haze-degraded images (inference time). As observed, while FFA-Net, Restormer, Trinity-Net, and DehazeFormer deliver superior dehazing outcomes, they come with larger Params and FLOPs, which can constrain their practical deployment. On the other hand, AOD-Net and Light-DehazeNet boost smaller parameter sizes, albeit at the expense of considerable performance loss. When compared to FCTF-Net and DCRD-Net, both designed specifically for RSID, the proposed DVKT framework exhibits the lowest computational cost and inference time, underscoring its advantage. Notably, the proposed DVKT framework achieves almost identical PSNR and SSIM values to the teacher model SCSNet but with reduced computational requirements and faster inference times.