Single-image dehazing techniques are extensively used in outdoor optical image acquisition equipment. Most existing methods pay attention to use various priors to estimate scene transmission. In this paper, a fast single-image dehazing algorithm is proposed based on a piecewise transformation model between the minimum channels of the hazy image and the haze-free image in optical model. The minimum channel of the haze-free image is obtained by the piecewise transformation, which is a quadratic function model that we establish for the dark region, and a linear transformation model is established for the bright region. Using the minimum channels of the hazy image and the haze-free image, a transmission estimation model is established based on the haze optical model with adjustment variables. To obtain an accurate estimation of atmospheric light, we estimate the atmospheric light twice. Finally, the haze-free image is restored. Experimental results show that the proposed algorithm has minimal halo artifacts and color distortion in various depths of field, flat areas and sky areas. From the subjective evaluation, objective evaluation and running time analysis, it can be seen that the algorithm in this paper is superior to most existing technologies.

中文翻译：

---

光学模型中基于分段变换的快速单图像去雾算法

单图像去雾技术被广泛用于室外光学图像采集设备中。大多数现有方法都注意使用各种先验来估计场景传输。在光学模型中，基于模糊图像最小通道和无雾图像最小通道之间的分段变换模型，提出了一种快速的单图像去雾算法。无雾图像的最小通道是通过分段变换获得的，分段变换是我们针对暗区域建立的二次函数模型，而针对亮区域建立了线性变换模型。使用雾度图像和无雾度图像的最小通道，基于具有调节变量的雾度光学模型，建立透射估计模型。为了获得对大气光的准确估算，我们估算了两次大气光。最后，恢复无雾图像。实验结果表明，该算法在不同景深，平坦区域和天空区域具有最小的光晕伪影和颜色失真。从主观评价，客观评价和运行时间分析可以看出，本文算法优于大多数现有技术。