Adaptive Image Dehazing Algorithm Based on Mean Unequal Relation Optimization
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摘要:
针对暗通道先验去雾算法的不足，如天空区域透射率估计过小和在景深突变处易发生光晕效应，该文提出一种新颖且高效的去雾算法。首先通过几何分析建立雾图对应无雾图像暗通道图的平面扇形模型，然后设定一种新型的高斯均值函数，对其标准差进行自适应处理，用以估计扇形模型的上下边界值，通过引入均值不等关系对两侧边界进行逼近，拟合出最优无雾图像暗通道图，进一步求得最佳透射率，同时也改进局部大气光的探索方法并复原出最终结果。实验表明，与其它一些经典算法相比较，所提算法能广泛适用于各类图像，去雾程度彻底且效果清晰自然，具有较低的时间复杂度，有利于实时处理。
Abstract:In view of shortcomings of dark channel prior dehazing methods, such as transmission in sky areas is small and halo effects are prone to occur in the edges, this paper proposes a novel and efficient dehazing algorithm. Firstly, the fan-shaped model with dark channel map of haze-free image is established by geometric analysis. Then a new Gaussian mean function is set to estimate the boundary values of the model and its standard deviation is adaptive processing. Mean-value unequal relationship is also introduced to approximate the two-sided boundary, which is used to fit the most excellent dark channel map of haze-free, further obtains the best transmission. At the same time the local atmospheric light is improved to recover the final result. Experimental results show that the proposed method can be widely applied to all kinds of images compared with other classical algorithms. The degree of dehazing is thorough, final result is clear and natural. More importantly, it is favorable for real-time processing that has low time complexity.
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Key words:
- Image restoration /
- Dehazing /
- Dark Channel Prior (DCP) /
- Transmission /
- Atmospheric scattering model
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表 1 改进的大气光探索方法
输入：有雾图像${{I}^c}(x)$； 步骤 1 找出有雾图像的3颜色通道的最大值${A}_{\max }^c(x) = \mathop {\max }\limits_{c \in \{\rm r,g,b\} } {{I}^c}(x)$ 步骤 2 进行形态学闭操作，滤波核尺寸分别为${r_1} = \min [w,h]/5$, ${r_2} = \min [w,h]/20$，得到两次闭操作结果${s_1}$和${s_2}$； 步骤 3 求取两次闭操作的平均值，$s = ({s_1} + {s_2})/2$ ； 步骤 4 进行交叉滤波平滑处理，得到最后的结果${{A}^c}$。 
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表 2 各个算法的
$e$ 和$r$ 指标对比图像 He[9]算法 Meng[11]算法 Ren[13]算法 Cai[12]算法 Sun[16]算法 本文算法 e r e r e r e r e r e r 1 4.50 1.28 5.82 1.79 7.55 1.47 2.76 1.08 6.44 1.22 9.01 1.41 2 8.44 1.69 5.36 2.48 20.71 1.52 17.87 1.56 15.74 1.49 18.68 1.81 3 13.89 1.70 22.56 2.59 10.82 1.97 9.11 1.47 11.22 2.01 21.83 2.01 4 10.83 1.48 24.93 3.77 27.00 3.01 9.87 1.36 12.74 1.99 22.63 2.22 5 6.87 1.28 12.12 1.69 15.61 1.76 11.10 1.28 17.25 2.06 17.18 1.64 6 26.23 1.73 31.11 1.90 31.36 2.60 18.85 1.30 22.75 1.94 30.04 2.38 7 15.51 1.85 38.03 4.12 20.35 2.55 14.53 1.63 24.74 2.98 18.47 2.95 8 3.69 1.41 3.12 1.58 8.94 1.79 2.49 1.13 6.33 1.74 8.56 1.42 均值 11.24 1.55 17.88 2.49 17.79 2.08 11.82 1.35 14.65 1.93 18.30 1.98
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表 3 各个算法的
$\theta $ 和$T(s)$ 指标对比图像 He[9]算法 Meng[11]算法 Ren[13]算法 Cai[12]算法 Sun[16]算法 本文算法 $\theta $ T $\theta $ T $\theta $ T $\theta $ T $\theta $ T $\theta $ T 1 0.00018 2.51 0.00651 3.80 0 4.27 0.00931 3.01 0.00347 2.47 0.00001 2.65 2 0.00022 2.56 0.00355 3.16 0 3.05 0 2.87 0.00019 2.67 0.00001 2.04 3 0.00031 2.38 0.00066 3.08 0 3.78 0.00197 2.94 0.00162 2.01 0 2.06 4 0 2.61 0.00003 4.54 0 4.60 0.00126 2.98 0.00276 2.39 0 2.07 5 0.00036 2.46 0.00004 3.50 0.00013 2.67 0 4.01 0 2.00 0 2.07 6 0.00161 2.80 0 4.40 0 3.36 0.00118 3.68 0.00019 2.17 0 2.09 7 0.00009 3.02 0.00014 5.10 0 3.22 0 3.31 0 2.57 0 2.43 8 0.00294 3.94 0.00079 6.55 0.00018 3.34 0.00169 7.34 0.00024 2.77 0.00016 2.55 均值 0.00071 2.78 0.00146 4.27 0.00003 3.53 0.00192 3.77 0.00105 2.38 0.00002 2.25
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