Acta Optica Sinica, Volume. 39, Issue 11, 1110002(2019)
Image Dehazing Algorithm Based on Convolutional Neural Network and Dynamic Ambient Light
Figures & Tables(12)
Fig. 2. Coarse segmentation of ambient light images. (a) Hazy images; (b) histograms of
Fig. 3. Dynamic ambient light. (a) Hazy images; (b) rough ambient light maps; (c) refined ambient light maps
Fig. 4. Examples of training set. (a) Real hazy images; (b) transmittance images; (c) paired training samples
Fig. 6. Estimation and refinement of transmittance. (a) Hazy images; (b) transmittance estimated by TEN; (c) refined transimittance
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
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
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
Table 1. Comparison of average objective indexes of dehazing images using the trained network with different training sets
View tableTable 1. 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
Table 2. Comparison of average objective indexes of dehazing images with different algorithms
View tableTable 2. 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
Table 3. Comparison of objective indicators of dehazing images with different algorithms
View tableTable 3. 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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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
Paper Information
Category: Image Processing
Received: May. 31, 2019
Accepted: Jul. 24, 2019
Published Online: Nov. 6, 2019
The Author Email: Liu Jieping (eeliujp@scut.edu.cn)
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