Atmospheric conditions induced by suspended particles such as fog, smog, rain, haze etc., severely affect the scene appearance and computer vision applications. In general, existing defogging algorithms use various constraints for fog removal. The efficiency of these algorithms depends on the accurate estimation of the depth models and the perfection of these models solely relies on pre-calculated coefficients through the training data. However, the depth model developed on the basis of these pre-calculated coefficients for dehazing may provide better accuracy for some kind of images but not equally well for every type of images. Therefore, training data-independent based depth model is required for a perfect haze removal algorithm. In this paper, an effective haze removal algorithm is reported for removing fog or haze from a single image. The proposed algorithm utilizes the atmospheric scattering model in fog removal. Apart from this, linearity in the depth model is achieved by the ratio of difference and sum of the intensity and saturation values of the input image. Besides, the proposed method also take care the well-known problems of edge preservation, white region handling and colour fidelity. Experimental results show that the proposed model is more efficient in comparison to the existing haze removal algorithms in terms of qualitative and quantitative analysis.

由悬浮颗粒（例如雾，烟雾，雨，雾霾等）引起的大气条件会严重影响场景外观和计算机视觉应用。通常，现有的除雾算法使用各种约束来去除雾。这些算法的效率取决于对深度模型的准确估计，而这些模型的完善仅取决于训练数据中预先计算的系数。但是，基于这些用于消雾的预先计算的系数开发的深度模型可能为某些类型的图像提供更好的精度，但对于每种类型的图像却不能提供同样好的效果。因此，理想的雾度去除算法需要基于训练数据的深度模型。在本文中，报告了一种有效的除雾算法，用于从单个图像去除雾或雾。该算法利用大气散射模型去除雾气。除此之外，深度模型中的线性是通过输入图像的强度和饱和度值之差与总和之比实现的。此外，该方法还解决了边缘保留，白色区域处理和色彩保真度等众所周知的问题。实验结果表明，在定性和定量分析方面，该模型与现有的除雾算法相比，效率更高。白色区域处理和色彩保真度。实验结果表明，在定性和定量分析方面，该模型与现有的除雾算法相比，效率更高。白色区域处理和色彩保真度。实验结果表明，在定性和定量分析方面，该模型与现有的除雾算法相比，效率更高。