结合卷积神经网络与动态环境光的图像去雾算法 
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Image Dehazing Algorithm Based on Convolutional Neural Network and Dynamic Ambient Light
华南理工大学电子与信息学院, 广东 广州 510641
图 & 表
图 1. 算法框架
Fig. 1. Framework of the proposed algorithm
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图 2. 环境光图粗分割。(a)雾霾图像;(b) V通道直方图;(c)区域分割
Fig. 2. Coarse segmentation of ambient light images. (a) Hazy images; (b) histograms of V channel; (c) regional segmentation
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图 3. 动态环境光。(a)雾霾图像;(b)粗环境光图;(c)细化环境光图
Fig. 3. Dynamic ambient light. (a) Hazy images; (b) rough ambient light maps; (c) refined ambient light maps
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图 4. 部分训练集示例。(a)真实雾霾图像;(b)透射率图像;(c)配对训练样本
Fig. 4. Examples of training set. (a) Real hazy images; (b) transmittance images; (c) paired training samples
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图 5. TEN结构
Fig. 5. Structure of TEN
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图 6. 透射率的估计与细化。(a)雾霾图像;(b) TEN估计的透射率;(c)细化后的透射率
Fig. 6. Estimation and refinement of transmittance. (a) Hazy images; (b) transmittance estimated by TEN; (c) refined transimittance
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图 7. 透射率估计效果对比。(a)雾霾图像;(b) TEN2透射率;(c) TEN1透射率;(d) TEN2去雾结果;(e) TEN1去雾结果
Fig. 7. Comparison of transmittance estimation effects. (a) Hazy images; (b) transmittance estimated by TEN2; (c) transmittance estimated by TEN1; (d) restored results of TEN2; (e) restored results of TEN1
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图 8. 全局大气光和动态环境光复原效果。(a)雾霾图像;(b)全局大气光去雾结果;(c)动态环境光去雾结果
Fig. 8. Restored effects of global and dynamic ambient light. (a) Hazy images; (b) results restored by global atmospheric light; (c) results restored by dynamic ambient light
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图 9. 不同算法的去雾结果。(a)雾霾图像;(b)文献[
4-5]中算法;(c)文献[
6]中算法;(d)文献[
9]中算法;(e)文献[
10]中算法;(f)本文算法
Fig. 9. Dehazing results of different algorithms. (a) Hazy images; (b) method in Ref. [4-5]; (c) method in Ref. [6]; (d) method in Ref. [9]; (e) method in Ref. [10]; (f) proposed algorithm
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表 1不同训练集训练网络的去雾图像客观指标均值比较
Table1. Comparison of average objective indexes of dehazing images using the trained network with different training sets
| Training set | Standard deviation | Average gradient | Information entropy | Edge intensity |
|---|
| Ref. [6] | 60.21 | 8.22 | 7.21 | 59.26 | | Ref. [9] | 59.23 | 8.05 | 7.22 | 58.07 | | Ref. [7] | 62.59 | 8.17 | 7.22 | 58.80 | | Ref. [18] | 64.02 | 9.55 | 7.21 | 69.03 |
|
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表 2不同去雾算法去雾图像客观指标均值比较
Table2. Comparison of average objective indexes of dehazing images with different algorithms
| Algorithm | Standard deviation | Average gradient | Information entropy | Edge intensity |
|---|
| Method in Ref. [4-5] | 45.12 | 7.82 | 7.02 | 57.07 | | Method in Ref. [6] | 51.35 | 7.65 | 7.14 | 55.50 | | Method in Ref. [9] | 51.41 | 7.13 | 7.16 | 51.34 | | Method in Ref. [10] | 51.32 | 8.04 | 7.20 | 57.82 | | Proposed method | 64.02 | 9.55 | 7.21 | 69.03 |
|
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表 3不同去雾算法的去雾图像客观指标比较
Table3. Comparison of objective indicators of dehazing images with different algorithms
| Image | Algorithm | Standard deviation | Average gradient | Information entropy | Edge intensity |
|---|
| Img1 | Method in Ref. [4-5] | 52.95 | 7.47 | 7.40 | 58.53 | | Method in Ref. [6] | 61.70 | 7.49 | 7.34 | 59.37 | | Method in Ref. [9] | 66.51 | 6.95 | 7.19 | 55.70 | | Method in Ref. [10] | 62.45 | 7.27 | 7.34 | 58.09 | | Proposed method | 76.28 | 9.00 | 7.54 | 71.62 | | Img2 | Method in Ref. [4-5] | 45.33 | 13.02 | 7.47 | 89.47 | | Method in Ref. [6] | 49.40 | 11.29 | 7.33 | 76.85 | | Method in Ref. [9] | 37.43 | 8.90 | 7.09 | 62.69 | | Method in Ref. [10] | 57.02 | 13.05 | 7.53 | 88.99 | | Proposed method | 70.11 | 15.25 | 7.63 | 104.10 | | Img3 | Method in Ref. [4-5] | 55.74 | 12.98 | 7.41 | 86.35 | | Method in Ref. [6] | 55.60 | 12.96 | 7.61 | 86.28 | | Method in Ref. [9] | 52.08 | 10.85 | 7.52 | 74.17 | | Method in Ref. [10] | 67.54 | 14.61 | 7.62 | 97.78 | | Proposed method | 77.63 | 18.01 | 7.23 | 119.85 | | Img4 | Method in Ref. [4-5] | 49.30 | 17.69 | 7.54 | 119.87 | | Method in Ref. [6] | 49.96 | 15.82 | 7.56 | 107.62 | | Method in Ref. [9] | 56.78 | 15.93 | 7.60 | 106.44 | | Method in Ref. [10] | 59.89 | 18.47 | 7.75 | 120.96 | | Proposed method | 76.47 | 22.08 | 7.60 | 149.85 | | Img5 | Method in Ref. [4-5] | 49.67 | 9.81 | 7.27 | 65.23 | | Method in Ref. [6] | 49.98 | 10.22 | 7.29 | 67.33 | | Method in Ref. [9] | 50.89 | 9.40 | 7.29 | 65.60 | | Method in Ref. [10] | 54.45 | 9.89 | 7.24 | 69.11 | | Proposed method | 61.95 | 12.11 | 7.36 | 78.80 | | Img6 | Method in Ref. [4-5] | 33.07 | 5.11 | 6.82 | 35.96 | | Method in Ref. [6] | 51.43 | 5.45 | 7.35 | 38.38 | | Method in Ref. [9] | 59.43 | 4.70 | 7.57 | 34.38 | | Method in Ref. [10] | 50.14 | 5.03 | 7.40 | 36.77 | | Proposed method | 72.29 | 6.75 | 7.63 | 46.94 |
|
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刘杰平, 杨业长, 陈敏园, 马丽红. 结合卷积神经网络与动态环境光的图像去雾算法[J]. 光学学报, 2019, 39(11): 1110002. Jieping Liu, Yezhang Yang, Minyuan Chen, Lihong Ma. Image Dehazing Algorithm Based on Convolutional Neural Network and Dynamic Ambient Light[J]. Acta Optica Sinica, 2019, 39(11): 1110002.
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