基于均值不等關(guān)系優(yōu)化的自適應(yīng)圖像去霧算法
doi: 10.11999/JEIT190368
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蘭州交通大學(xué)電子與信息工程學(xué)院 蘭州 730070
Adaptive Image Dehazing Algorithm Based on Mean Unequal Relation Optimization
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School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
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摘要:
針對(duì)暗通道先驗(yàn)去霧算法的不足，如天空區(qū)域透射率估計(jì)過(guò)小和在景深突變處易發(fā)生光暈效應(yīng)，該文提出一種新穎且高效的去霧算法。首先通過(guò)幾何分析建立霧圖對(duì)應(yīng)無(wú)霧圖像暗通道圖的平面扇形模型，然后設(shè)定一種新型的高斯均值函數(shù)，對(duì)其標(biāo)準(zhǔn)差進(jìn)行自適應(yīng)處理，用以估計(jì)扇形模型的上下邊界值，通過(guò)引入均值不等關(guān)系對(duì)兩側(cè)邊界進(jìn)行逼近，擬合出最優(yōu)無(wú)霧圖像暗通道圖，進(jìn)一步求得最佳透射率，同時(shí)也改進(jìn)局部大氣光的探索方法并復(fù)原出最終結(jié)果。實(shí)驗(yàn)表明，與其它一些經(jīng)典算法相比較，所提算法能廣泛適用于各類圖像，去霧程度徹底且效果清晰自然，具有較低的時(shí)間復(fù)雜度，有利于實(shí)時(shí)處理。
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關(guān)鍵詞:
- 圖像復(fù)原 /
- 去霧 /
- 暗通道先驗(yàn) /
- 透射率 /
- 大氣散射模型
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 改進(jìn)的大氣光探索方法
輸入：有霧圖像${{I}^c}(x)$； 步驟 1 找出有霧圖像的3顏色通道的最大值${A}_{\max }^c(x) = \mathop {\max }\limits_{c \in \{\rm r,g,b\} } {{I}^c}(x)$ 步驟 2 進(jìn)行形態(tài)學(xué)閉操作，濾波核尺寸分別為${r_1} = \min [w,h]/5$, ${r_2} = \min [w,h]/20$，得到兩次閉操作結(jié)果${s_1}$和${s_2}$； 步驟 3 求取兩次閉操作的平均值，$s = ({s_1} + {s_2})/2$ ； 步驟 4 進(jìn)行交叉濾波平滑處理，得到最后的結(jié)果${{A}^c}$。 
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表 2 各個(gè)算法的
$e$ 和$r$ 指標(biāo)對(duì)比圖像 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 各個(gè)算法的
$\theta $ 和$T(s)$ 指標(biāo)對(duì)比圖像 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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