## 1 Introduction  
High-resolution remote sensing images have been widely used in Earth observation tasks in recent years [1]. Clouds and cloud shadows often exist during the process of image acquisition [2; 3; 4]. In a cloud area, an underlying object's reflection will be obstructed from reaching the sensors, leading to missing object information. Especially when landslides and hazardous events happen, cloud and cloud remote sensing images are very valuable for rescue purposes [5]. Discovering how to restore the shaded information is very significant. Thus, a cloud shadow area where the brightness is lower than that of other areas will be formed [6; 7]. Because there is a certain distance between a cloud and the ground, ground objects in the cloud shadow area can also receive some scattered light energy from the sky [8; 9]. As a result, with the help of the scattered light, the ground object's information can be partially presented compared to the shadow of the ground object. However, the influence of a cloud shadow varies according to the thickness and height of the cloud. In general, the thicker a cloud is, the more pronounced the cloud shadow is. The more centrally the object is located, the less scattered light it receives. Thus, in one cloud shadow area, the shelter of the thick cloud is more serious than that of the thin cloud [10; 11]. Moreover, since the cloud is thinner around the cloud edge area, the shelter in the central area is more serious than the edge area. As a result, in the same cloud shadow area, the information loss is uneven. In high-resolution remote sensing images, the ground object information in the cloud shadow is also asymmetrical. Therefore, it is of great significance to study the balanced shadow compensation method for asymmetrical cloud shadow areas to improve image utilization. The proposed cloud shadow removal methods can be classified into two types: cloud-based methods and the shadow-based method. In middle-resolution images, the cloud and cloud shadow are located adjacently [12; 13]. Therefore, cloud shadow removal methods are proposed together with cloud removal. They can be classified into four types: spatial-information-based, spectral-information-based, temporal-information-based, and hybrid methods [14]. Temporal-information-based methods are designed for thick cloud removal in large cloud areas. The main algorithm is to replace the cloud and cloud shadow regions with another cloud-free image acquired at another time [15]. However, the temporal differences between multitemporal images lead to spectral reflection changes [16]. Moreover, these methods may be restricted by cloud-free image requirements. Spectral-information-based methods aim to recover the spectral information loss caused by sensor failure or the presence of thin clouds in the multispectral or hyperspectral data, such as through the use of a moderate-resolution imaging spectroradiometer (MODIS). The missing information can be reconstructed through the correlated spectral bands [17]. For example, since MODIS band 6 is correlated with snow- or cloud-covered areas, [PERSON] et al. [18] recovered missing information with the calibrated and geo-located Terra MODIS bands 6 and 7. Spatial-information-based methods are used for single high-resolution remote sensing images. The spatial relationship between the local and non-local regions can be incorporated into cloud and cloud shadow removal. [PERSON] et al. formulated cloud and cloud shadow detection as a semantic segmentation problem and proposed a deep convolutional neural network (CNN)-based method to detect them in Landsat imagery [19]. The CNN-based method extracted multi-level spatial and spectral features from the global image and all the bands based on the cloud, cloud shadow and clear labels. These deep multi-level features are deconvolved to realize detailed segmentation to recognize clouds and cloud shadows.