## 1 Introduction  
High-quality images not only bring visual appeal but also provide a reliable source of information for visual systems. However, due to the complexity of weather and environmental conditions, outdoor images often suffer from degradation caused by various adverse factors. In recent years, the frequent occurrence of sandstorms has severely impacted daily life and posed challenges to the visual quality of outdoor imaging systems and the reliability of subsequent computer vision tasks [1, 2]. Under sandstorm conditions, the absorption and scattering of light by airborne dust particles results in images with a predominant yellow or red tint and a significant reduction in the visibility of distant objects. Numerous researchers have focused on developing sandstorm image enhancement algorithms to restore the color and details of sandstorm images. Existing sand-dust image enhancement techniques can be divided into three main types: nonphysical model-based methods, deep learning-based methods, and physical model-based models. Nonphysical model-based sandstorm image enhancement methods have predominantly concentrated on color correction and contrast enhancement [3; 4; 5]. For example, [PERSON] et al. [6] proposed an image enhancement algorithm based on channel compensation and brightness partitioning. However, these methods often introduce new chromatic artifacts and exhibit limited de-dust capabilities, primarily due to neglecting the inherent physical characteristics of sandstorm images. In recent years, data-driven approaches have succeeded in image processing tasks such as dehazing and underwater image enhancement. Consequently, researchers have explored their application in sandstorm image restoration [7; 8]. However, these methods rely on synthetic datasets, which often fail to generalize to real-world sandstorm images due to domain gaps. In response, some unsupervised frameworks have been proposed [9; 10], yet their performance on severely degraded sand-dust images is limited. To reduce the reliance on large-scale datasets and improve the generalization to real-world sandstorm images, a robust paradigm has been proposed that involves the integration of priors with the atmospheric scattering model [11; 12]. Moreover, [PERSON] et al. proposed a real-time scene restoration framework, incorporating the rank one prior (ROP) [13] to restore degraded images under varying imaging conditions, and they further enhanced its generalization with the rank one prior plus (ROP+) [14]. However, these methods can struggle to deliver satisfactory results when the priors are invalid. In summary, while existing methods have successfully mitigated the detrimental effects of sandstorms, several issues persist. Specifically, in severe sandstorm conditions, the current color correction techniques do not sufficiently account for the wavelength-dependent attenuation of color channels, limiting their effectiveness in eliminating the red veil. Moreover, the priors used during the sand-dust removal process do not fully consider the physical properties of sandstorm images, leading to color artifacts. To resolve the issues above, this paper conducts in-depth research. In the color correction process, channel compensation is initially applied to mitigate color shifts, followed by color transfer based on the statistical properties of the mean and variance of color channels, effectively eliminating the red veil. During dust removal, we improve the boundary constraints of the saturation line prior by appropriately adjusting the atmospheric light map, significantly reducing chromatic artifacts. Comprehensive experiments conducted on real-world sandstorm images demonstrate the robustness and effectiveness of the proposed algorithm. The contributions of this research are as follows:  
* We present a robust sand-dust image restoration algorithm that decomposes the restoration task into color correction and dust removal components. In mild dust conditions, the proposed method delivers enhanced dust removal effectiveness.