Acta Optica Sinica, Volume. 41, Issue 11, 1133002(2021)
Color Constancy with Multi-Channel Confidence-Weighted Method
Figures & Tables(12)
Fig. 4. Comparison of the number of confidence-weighted areas of single-channel and three-channel light source
Fig. 9. Visualization of the network output. (a) Input images; (b) using proposed algorithm to estimate images corrected by light source; (c) weight distribution diagrams of proposed algorithm to weight the image multi-channel confidence; (d) standard light source; (e) Grey-world algorithm; (f) White-Patch algorithm; (g) Shades-of-Grey algorithm; (h) Grey-Edge algorithm
Table 1. Comparison of the number of network parameters between proposed algorithm and other algorithms
View tableTable 1. Comparison of the number of network parameters between proposed algorithm and other algorithms
Method Number of parameters /M Lou[ 22 ]56.9 CNN[ 7 ]14.9 DS-Net[ 23 ]4.2 FC4(AlexNet)[ 14 ]2.9 FC4(SqueezeNet)[ 14 ]1.2 IEN+PSN[ 21 ]1.6 Proposed method 0.7
Table 2. Test error results using NUS-8 camera data set
View tableTable 2. Test error results using NUS-8 camera data set
Method Mean Median Triple mean Best 25% Worst 25% Grey-word[ 26 ]4.140 3.200 3.390 0.900 9.000 White-Patch[ 27 ]10.620 10.580 10.490 1.860 19.450 Shades-of-Grey[ 28 ]3.400 2.570 2.730 0.770 7.410 1-order Grey Edge[ 4 ]3.200 2.220 2.430 0.720 7.360 2-order Grey Edge[ 4 ]3.200 2.260 2.440 0.750 7.270 Pixel-based Gamut[ 29 ]7.700 6.710 6.900 2.510 14.050 Edge-based Gamut[ 29 ]8.430 7.050 7.370 2.410 16.080 Bayesian[ 6 ]3.670 2.730 2.910 0.820 8.210 Using CNNs[ 7 ]7.600 6.900 7.400 3.000 12.400 Deep color constancy[ 22 ]6.200 5.000 5.400 3.900 8.600 CCC[ 30 ]2.800 1.800 1.900 0.850 6.300 CC-GANs(pix-pix)[ 13 ]3.800 3.000 3.700 1.900 8.400 FC4-AlexNet[ 14 ]2.120 1.530 1.670 0.480 4.780 FC4-SqueezeNet[ 14 ]IEN+PSN[ 21 ]Multi-Hypothesis[ 31 ]2.230 2.100 2.350 1.570 1.350 1.550 1.270 1.510 1.730 0.470 0.450 0.460 5.150 5.010 5.620 Proposed method 1.566 1.032 1.162 0.352 3.472
Table 3. Test error results using the reprocessed ColorChecker data set
View tableTable 3. Test error results using the reprocessed ColorChecker data set
Method Mean Median Triple mean Best 25% Worst 25% 95th Grey-world[ 26 ]10.700 10.600 10.700 3.450 12.300 17.400 White-Patch[ 27 ]9.800 8.000 8.900 3.800 13.600 22.300 Shades-of-Grey[ 28 ]8.300 7.500 7.800 2.900 11.800 17.000 1-order Grey Edge[ 4 ]5.000 3.700 4.100 3.900 10.100 13.300 2-order Grey Edge[ 4 ]5.400 4.500 4.800 2.600 9.800 12.800 Pixel-based Gamut[ 29 ]6.900 5.200 5.700 1.800 11.700 18.200 Edge-based Gamut[ 29 ]6.900 4.600 5.200 2.100 14.600 20.600 Bayesian[ 6 ]General Grey-World[ 32 ]6.600 7.600 4.600 6.700 5.200 7.000 3.200 3.800 10.900 12.100 18.400 16.500 Using CNNs[ 7 ]8.200 6.300 6.800 2.600 11.300 20.400 Deep color constancy[ 22 ]Exemplar-Based[ 33 ]5.700 2.890 4.700 2.270 5.000 2.420 3.200 0.820 8.400 5.970 12.400 6.950 CCC(dist+ext)[ 30 ]2.000 1.220 1.400 0.350 4.760 5.850 CC-GANs(Pix2Pix)[ 13 ]3.600 2.800 3.100 1.200 7.200 9.400 FC4-AlexNet[ 14 ]1.770 1.110 1.290 0.340 4.290 5.440 FC4-SqueezeNet[ 14 ]IEN+PSN[ 21 ]Multi-Hypothesis[ 31 ]1.650 2.250 2.100 1.180 1.590 1.320 1.270 1.730 1.530 0.380 0.590 0.360 3.780 5.030 5.100 4.730 6.080 — Proposed method 1.574 1.030 1.119 0.300 3.475 4.039
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Zepeng Yang, Kai Xie, Tong Li, Mengyao Yang, Bin Yang. Color Constancy with Multi-Channel Confidence-Weighted Method[J]. Acta Optica Sinica, 2021, 41(11): 1133002
Paper Information
Category: Vision, Color, and Visual Optics
Received: Dec. 4, 2020
Accepted: Jan. 18, 2021
Published Online: Jun. 7, 2021
The Author Email: Xie Kai (2596898130@qq.com)
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