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摘要: 根据雾气浓度的视觉特征，提出一种雾气浓度估计模型.在此基础上，结合大气散射模型，提出一种新的图像去雾算法.首先，基于雾气浓度估计模型计算出雾气浓度量化图，利用模糊聚类算法在量化图中识别出雾气最浓区域并估计出全球光; 然后，对量化图中的“非雾气最浓”区域再次进行聚类处理，根据文中所提最优透射率评价指标估计出每个聚类单元的透射率，将全球光与透射图以及有雾图像导入散射模型，便可达到去雾的目的; 最后，针对去雾后图像较实际场景偏暗，提出一种基于小波域的多尺度锐化算法进行增强处理，以改善其主观视觉质量.实验结果表明，本文算法与现有主流算法相比，具有更好的去雾效果，并且其计算速度也相对较快.Abstract: This paper proposes a haze thickness estimation model based on visual characteristics of haze thickness, and combines this model with atmosphere scattering model to present an innovative image dehazing algorithm. First, a haze thickness quantitative map is calculated via the haze thickness estimation model, from which the thickest area is identified by the fuzzy clustering algorithm and global atmospheric light is estimated. After that, the algorithm carries on clustering processing towards the non-thickest area in the quantitative map, and estimates the transmission of each cluster unit according to the optimized transmission evaluation index mentioned in this paper. The haze-free image can be restored from scattering model with global light, refined transmission map and original hazy image. At last, we propose a multi-scale sharpening algorithm based on wavelet domain to make up for the defect that the haze-free image is dark-look so as to improve the visual effect. Several numerical experiments demonstrate that the proposed method outperforms the mainstream dehazing algorithms in daze removal effect at a much lower implementation cost.
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图 1 现有去雾算法的局限性((a), (e), (i)有雾图像; (b)Tan算法; (c)Nishino算法; (f)Fattal算法; (g)He算法; (j)Tarel算法; (k)Pang算法; (d), (h), (l)本文算法)
Fig. 1 The limitations of the existing algorithms ((a), (e), (i) Hazy image; (b) Tan; (c) Nishino; (f) Fattal; (g) He; (j) Tarel; (k) Pang; (d), (h), (l) Proposed)
图 2 雾气浓度估计((a)有雾图像; (b)粗糙雾气浓度量化图; (c)雾气浓度量化图)
Fig. 2 Haze thickness estimation ((a) Hazy images; (b) Rough haze thickness quantitative maps; (c) Refined haze thickness quantitative maps)
图 3 全球光定位中间过程((a)有雾图像; (b)雾气浓度量化图; (c)预选区域; (d)平坦分布图; (e)候选区域)
Fig. 3 The intermediate process of global light localization ((a) Hazy image; (b) Refined haze thickness quantitative map; (c) Pre-selected region; (d) Flat distribution map; (e) Candidate region)
图 4 不同全球光对应的去雾效果比较((a)位置示意图; (b) Namer算法; (c) He算法; (d) Kim算法; (e)本文算法)
Fig. 4 Dehazed images by different global light estimation methods ((a) Location schematic diagram; (b) Namer; (c) He; (d) Kim; (e) Proposed)
图 5 卫星图像去雾实验((a)有雾图像; (b)对比度最大; (c)本文所提指标Ψ)
Fig. 5 Satellite image dehazing experiment ((a) Hazy image; (b) Maximum contrast; (c) Index Ψ)
图 6 标准差、饱和度以及指标Ψ的变化曲线
Fig. 6 The curves of standard deviation, saturation, and index Ψ
图 8 对比分析((a)基于指标Ψ与邻域估计法得到的透射图; (b)基于指标Ψ与聚类估计法得到的透射图; (c)透射图(a)对应的去雾效果; (d)透射图(b)对应的去雾效果; (e)基于黑色通道先验与邻域估计法得到的透射图; (f)基于黑色通道先验与聚类估计法得到的透射图; (g)透射图(e)对应的去雾效果; (h)透射图(f)对应的去雾效果)
Fig. 8 Comparative analysis ((a) Transmission map with neighborhood and index Ψ; (b) Transmission map with cluster unit and index Ψ; (c) Dehazed image using (a); (d) Dehazed image using (b); (e) Transmission map with neighborhood and dark channel prior; (f) Transmission map with cluster unit and dark channel prior; (g) Dehazed image using (e); (h) Dehazed image using (f))
图 9 各后处理算法的增强效果对比((a), (e)去雾后的图像; (b), (f) Yan算法; (c), (g)张登银算法; (d), (h)本文算法)
Fig. 9 Comparison of enhanced images by different post-processing algorithms ((a), (e) Dehazed images; (b), (f) Yan; (c), (g) Zhang; (d), (h) Proposed)
图 10 本文算法去雾效果(上:有雾图像; 中:透射图; 下:去雾效果)
Fig. 10 Dehazed images by proposed method (Top: hazy images; Middle: transmission map; Bottom: dehazed images)
表 1 图像质量评价指标
Table 1 Image quality evaluation parameters
实验对象 Ancuti算法 Zhu算法 Tarel算法 Tan算法 He算法 Meng算法 本文算法 评价指标 r S H r S H r S H r S H r S H r S H r S H Dolls 1.22 0.73 0.04 2.11 0.81 0.00 2.63 0.66 0.32 3.60 0.28 0.34 2.67 0.65 0.00 2.76 0.68 0.01 2.98 0.72 0.08 Manhattan 1.52 0.80 0.33 1.32 0.82 0.01 1.98 0.60 0.03 3.17 0.73 0.00 1.76 0.84 0.01 2.12 0.78 0.28 1.97 0.81 0.04 Cityscape 1.81 0.73 0.27 2.81 0.70 0.08 4.70 0.38 0.20 6.25 0.29 0.72 4.43 0.52 0.08 3.25 0.64 0.03 4.47 0.62 0.01 Mountain 1.08 0.67 0.01 1.25 0.78 0.02 1.80 0.80 0.01 2.06 0.74 0.05 1.13 0.82 0.00 1.66 0.83 0.17 1.46 0.84 0.01 Town 1.37 0.88 0.14 1.43 0.82 0.05 2.59 0.73 0.14 2.81 0.48 0.32 1.77 0.78 0.01 2.06 0.77 0.67 2.10 0.92 0.03 Pizza 1.54 0.90 0.17 1.07 0.92 0.01 1.71 0.79 0.03 3.00 0.32 0.72 1.30 0.96 0.02 1.37 0.90 0.15 1.18 0.93 0.00 
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