# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 1 Introduction  
Remote-sensing image dehazing (RSID), as a critical technique in remote-sensing image processing, aims to eliminate the blurring and degradation caused by atmospheric media [1]. With the widespread application of remote-sensing technology in meteorological monitoring, environmental assessment, and other fields, clear remote-sensing images are crucial for the accuracy and timeliness of information extraction. RSID technology not only enhances image quality but also plays a key role in meteorological monitoring [2], disaster response [3], and military surveillance [4]. In recent years, image-dehazing techniques have been extensively studied, and many dehazing methods have been proposed [5, 6]. Currently, these dehazing methods can be categorized into two types [7]: prior-based dehazing methods and learning-based dehazing methods. Prior-based dehazing methods relies on manually extracting statistical differences between haze-degraded images and clear images to establish empirical priors [8, 9, 10]. The most classical prior-based image-dehazing methods recover the clear image based on the atmospheric scattering model (ASM). For instance, [PERSON] et al. [11] raised the dark channel prior (DCP) method to estimate the transmission map for image dehazing. To reduce the model complexity, [PERSON] et al. [12] proposed a guided filtering method for image dehazing subsequently. In addition, [PERSON] et al. [13] proposed the color attenuation prior (CAP) method to recover depth information so as to conduct image dehazing. [PERSON] [14] noticedthat RGB values follow a color line prior (CLP) within each local patch in an image, and estimated the transmission map based on CLP. Although these prior-based methods are easy to implement, they rely on scene-specific prior assumptions, and when these assumptions are not met, the dehazing results may be unsatisfactory [15]. With the advancement of deep learning, learning-based dehazing methods have been widely explored [16; 17; 18], which mainly include atmospheric scattering model (ASM)-learning methods and end-to-end learning methods. ASM-learning methods achieve image dehazing by designing suitable deep neural networks to estimate the haze parameters in the ASM. For example, [PERSON] et al. [19] incorporated prior knowledge into a gradient-guided Swin Transformer [20] for haze parameter estimation. To avoid explicitly collecting paired training data, [PERSON] and [PERSON] [21] proposed an adversarial auto-augmentation approach by combining extended atmospheric scattering model. End-to-end learning methods achieve effective image dehazing by directly learning the underlying mapping relationship between haze-degraded images and clear images [22]. For example, [PERSON] et al. [23] developed a feature fusion attention network, named FFA-Net, for end-to-end image dehazing. [PERSON] et al. [24] proposed a feature modulation module for effectively integrating Transformer and CNN features. Despite the notable achievements of current learning-based approaches, most of them are designed for dehazing natural scenes [25; 26]. Compared to images in natural scenes, remote-sensing images are typically larger, more complex, and exhibit variable and irregular haze intensity distributions. Therefore, the application of existing end-to-end dehazing methods for haze-degraded remote-sensing images may not be ideal [27]. Recently, researchers have explored numerous methods specifically tailored for RSID [28], achieving significant results. For example, [PERSON] and [PERSON] [29] designed a two-stage coarse-to-fine dehazing network FCTF-Net for gradually refining dehazing result. [PERSON] et al. [30] introduced a detail-compensated split attention to capture global and local regional dependencies for RSID. [PERSON] and [PERSON] [31] proposed a dynamic collaborative reasoning learning framework for RSID. [PERSON] et al. [32] developed a frequency-oriented FOTformer for recovering haze-free images. [PERSON] et al.