近年来，基于物理模型的单图像去雾技术取得了显著的进展。

基于模型的去雾方法是从图像复原角度出发,需要考查图像退化原因,对大气散射作用进行建模分析,实现场景的复原。国内外很多研究人员分析和评估了大气对成像的影响，在这个过程中，散射理论发挥了极大的作用。由于单射模型和多射模型能够较为准确的表达出大气散射对雾天成像的影响，因此受到了广泛关注，并且已经成功用于恶劣天气条件下图像退化的建模中。这类方法基于大气散射规律，建立了图像退化模型，充分利用了退化的先验知识,所以具有内在的优越性。

自从1998年Oakley等人36]利用Mie大气散射定律对恶劣天气下图像去雾做了一些探索工作至今，基于模型的图像去雾方法已成为图像去雾处理领域研究的热点。同时我们也注意到，由于基于物理模型的复原方法处理的前提是已知景深信息，这就需要使用多张图像或其他更多辅助信息。

1.在无雾图像局部对比度远高于模糊图像的假设下，Tan提出了一种基于马尔可夫随机场(MRF)的图像局部对比度最大化的去雾新方法。虽然Tans方法能够取得令人印象深刻的结果，但它往往会产生过饱和的图像。

R. T. Tan, “Visibility in bad weather from a single image,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2008, pp. 1–8.

2.Fattal提出了基于独立分量分析(ICA)的彩色图像去雾方法，但该方法耗时长，不能用于灰度图像去雾。此外，对雾霾图像的处理也存在一定的困难。

R. Fattal, “Single image dehazing,” ACM Trans. Graph., vol. 27, no. 3, p. 72, Aug. 2008. 

3.灵感来自于广泛使用的黑色物体减法技术[28],基于大量的实验haze-free图像。

P. S. Chavez, Jr., “An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data,” Remote Sens. Environ., vol. 24, no. 3, pp. 459–479, Apr. 1988.

发现黑暗的通道(DCP)之前,在大多数non-sky补丁,至少一个颜色通道有一些像素的强度很低,接近于零。在此基础上，估算了雾霾的厚度，利用大气散射模型恢复了无雾霾图像。DCP方法在大多数情况下是简单而有效的。然而，它不能很好地处理天空图像和计算密集型。

K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 12, pp. 2341–2353, Dec. 2011.

针对DCP算法的不足，提出了改进的[30][36]、[45][51]算法。

S.-C. Pei and T.-Y. Lee, “Nighttime haze removal using color transfer pre-processing and dark channel prior,” in Proc. 19th IEEE Conf. Image Process. (ICIP), Sep./Oct. 2012, pp. 957–960. 

Y. Xiang, R. R. Sahay, and M. S. Kankanhalli, “Hazy image enhancement based on the full-saturation assumption,” in Proc. IEEE Conf. Multimedia Expo Workshops (ICMEW), Jul. 2013, pp. 1–4. 

J.-P. Tarel and N. Hautiere, “Fast visibility restoration from a single color or gray level image,” in Proc. IEEE 12th Int. Conf. Comput. Vis. (ICCV), Sep./Oct. 2009, pp. 2201–2208. 

K. Tang, J. Yang, and J. Wang, “Investigating haze-relevant features in a learning framework for image dehazing,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2014, pp. 2995–3002.

4.为了提高效率，Gibson等人[31]，Yu等人[32]，He等人[43]，Tarel和Hautiere [45]， Tarel等人[46]分别用标准中值滤波、中值滤波中值、导向联合双侧滤波[37]和导向图像滤波代替了耗时的软垫[44]。

[31] K. B. Gibson, D. T. Vo, and T. Q. Nguyen, “An investigation of dehazing effects on image and video coding,” IEEETrans. Image Process., vol. 12, no. 2, pp. 662–673, Feb. 2012.

[32] J. Yu, C. Xiao, and D. Li, “Physics-based fast single image fog removal,” in Proc. IEEE 10th Int. Conf. Signal Process. (ICSP), Oct. 2010, pp. 1048–1052. 

[43] K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 6, pp. 1397–1409, Jun. 2013.

[45] J.-P. Tarel and N. Hautiere, “Fast visibility restoration from a single color or gray level image,” in Proc. IEEE 12th Int. Conf. Comput. Vis. (ICCV), Sep./Oct. 2009, pp. 2201–2208. 

[46] J.-P. Tarel, N. Hautière, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” IEEE Intell. Transp. Syst. Mag., vol. 4, no. 2, pp. 6–20, Apr. 2012. 

[37] C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. 6th Int. Conf. Comput. Vis. (ICCV), Jan. 1998, pp. 839–846. 

[44] A. Levin, D. Lischinski, and Y. Weiss, “A closed-form solution to natural image matting,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 2, pp. 228–242, Feb. 2008.

5.在去雾质量方面，Kratz和Nishino[48]和Nishino等人[49]采用阶乘马尔可夫随机场对图像进行建模，以更准确地估计场景亮度;孟等人提出了一种有效的正则化去雾方法，通过挖掘图像固有的边界约束来恢复无雾图像

[48] L. Kratz and K. Nishino, “Factorizing scene albedo and depth from a single foggy image,” in Proc. IEEE 12th Int. Conf. Comput. Vis. (ICCV), Sep./Oct. 2009, pp. 1701–1708.

[49] K. Nishino, L. Kratz, and S. Lombardi, “Bayesian defogging,” Int. J. Comput. Vis., vol. 98, no. 3, pp. 263–278, Jul. 2012.