# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 1 Introduction
[33] achieved unsupervised RSID by introducing an enhanced attention-guided GAN network. [PERSON] et al. [34] designed a hierarchical sliced interactive multi-layer collaborative decoding network (HSMD-Net) to achieve image restoration by realizing cross-layer global and local information interaction. In general, most state-of-the-art methods employ heavy backbone networks (e.g., Transformer models [28]) to obtain better performance. However, due to the demanding computational cost and slow running speed, these models are highly inefficient. Therefore, a fundamental question is how to obtain efficient RSID models rather than relying on heavy backbone networks. Furthermore, in comparison to dehazing tasks within natural scenes, there exists a significant scarcity of RSID datasets that incorporate unambiguously labeled images, resulting in suboptimal performance. Although a large number of lightweight techniques, such as quantization [35] and pruning [36], have been explored, there is still no good way to solve the problem of scarcity of RSID datasets. Fortunately, the emergence of knowledge distillation technology provides an alternative for addressing these issues. As a result, this study proposes a Dual-View Knowledge Transfer (DVKT) framework for simultaneously distilling hierarchical features and cross-layer correlation knowledge. The framework encompasses two innovative modules: Intra-layer Knowledge Transfer (Intra-KT) and Inter-layer Knowledge Transfer (Inter-KT). Specifically, the Intra-KT module leverages representative features extracted from a well-trained teacher network to sequentially guide the corresponding features of the student network. This mechanism progressively corrects the learning bias of the student, enabling its features to exhibit similar visual and semantic patterns to those of the teacher. In contrast, the Inter-KT module calculates the relationships between features of the teacher network layer-by-layer and utilizes this knowledge to aid the student network in modulating its hierarchical features, thereby enabling the student to learn the solution process of the teacher. Based on the proposed DVKT framework, the distilled student network can effectively acquire compact and knowledge-rich features, resulting in high-quality haze-free remote-sensing images. With less than 6% of the parameters of the teacher model, the distilled model achieves nearly an 8-fold speedup while maintaining competitive dehazing performance. In summary, the main contributions and novelty of this work are three-fold:  
* A Dual-View Knowledge Transfer (DVKT) framework is proposed for effective RSID: to the extent of our knowledge, the proposed DVKT framework is the first work to concentrate on transferring shared knowledge from natural scene dehazing models to RSID models and learning remote-sensing scene-specific knowledge for lightweight models. * Two complementary knowledge transfer modules, Intra-KT and Inter-KT, are integrated to comprehensively transfer the structured knowledge from well-trained natural scene-dehazing models to lightweight RSID models. * Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed DVKT framework. In particular, the proposed DVKT framework achieves satisfactory dehazing results while significantly improving processing speed.