Scene recovery is a crucial imaging task that holds significant relevance in various practical domains, such as video surveillance and autonomous vehicles, among others. To improve the visual quality of sand-dust images captured under various weather and imaging situations, we propose the use of Anisotropic Guided Filter (AnisGF). This filter utilizes a weighted averaging technique for an image to minimize diffusion and preserve the sharpness of edges. The weights provided in this study are adjusted using local neighborhood variances in order to obtain robust anisotropic filtering, while the computational efficiency of the original guided filter is preserved. The proposed technique deals with the problem of detail halos and the management of inconsistent structures seen in earlier guided filter variants. Furthermore, experiments with sand-dust, haze and under water images enhancement also show how the AnisGF improves image quality in terms of quality metrics namely Blind/ Referenceless Image Spatial Quality Evaluator, Naturalness Image Quality Evaluator, and Perception based Image Quality Evaluator.