Illumination Estimation Based on Exemplar Learning in Logarithm Domain

Shuai Cui; Jun Zhang; Jun Gao

doi:10.3788/AOS201838.0233001

Journals >Acta Optica Sinica >Volume 38 >Issue 2 >Page 0233001 > Article

Acta Optica Sinica
Vol. 38, Issue 2, 0233001 (2018)

Illumination Estimation Based on Exemplar Learning in Logarithm Domain

Shuai Cui, Jun Zhang^*, and Jun Gao

Author Affiliations

School of Computer and Information, Hefei University of Technology, Hefei, Anhui 230009, China

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DOI: 10.3788/AOS201838.0233001 Cite this Article Set citation alerts

Shuai Cui, Jun Zhang, Jun Gao. Illumination Estimation Based on Exemplar Learning in Logarithm Domain[J]. Acta Optica Sinica, 2018, 38(2): 0233001 Copy Citation Text

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Fig. 1. Flow chart of algorithm

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Images and log-chrominance histograms of two scenes with three different illuminations. Scene 1 images and log-chrominance histograms under (a) white light, (b) blue light, (c) green light; scene 2 images and log-chrominance histograms under (d) white light, (e) blue light, (f) green light

Fig. 2. Images and log-chrominance histograms of two scenes with three different illuminations. Scene 1 images and log-chrominance histograms under (a) white light, (b) blue light, (c) green light; scene 2 images and log-chrominance histograms under (d) white light, (e) blue light, (f) green light

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Fig. 3. Illumination estimation process of single illuminant image

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Fig. 4. Illumination estimation of multi-illuminant images. (a) Original images; (b) ground-truth values; (c) single illuminant estimation results; (d) double illuminant estimation results; (e) multi-illuminant estimation results

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Fig. 5. Color correction results using different illumination estimation algorithms on SFU Grey-ball dataset. (a) Original images (b) Grey-World; (c) White-Patch; (d) Shades-of-Grey; (e) Grey-Edge; (f) Gamut Mapping; (g) Exemplar-Based; (h) proposed method

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χ² distance	Fig. 2(a)	Fig. 2(b)	Fig. 2(c)	Fig. 2(d)	Fig. 2(e)	Fig. 2(f)
Fig. 2(a)	-	-12.5997	-18.9088	-3.6787	-10.2847	-17.0799
Fig. 2(b)	-12.5997	-	-21.6092	-12.3675	-3.6276	-19.5451
Fig. 2(c)	-18.9088	-21.6092	-	-18.0153	-19.1128	-3.6758
Fig. 2(d)	-3.6787	-12.3675	-18.0153	-	-9.4169	-15.6009
Fig. 2(e)	-10.2847	-3.6276	-19.1128	-9.4169	-	-16.7831
Fig. 2(f)	-17.0799	-19.5451	-3.6758	-15.6009	-16.7831	-

Table 1. χ2 distance of log-chrominance histograms in Fig. 2

Method	Mean	Median	Trimean	Max
Do nothing	6.9	9.5	7.5	38.2
Grey-World	9.8	7.4	8.2	46.0
White-Patch	8.1	6.0	6.4	$\begin{matrix} \bar{36.3} \end{matrix}$
Shades-of-Grey	7.0	5.3	5.6	36.6
Grey-Edge	7.0	5.2	5.5	$\begin{matrix} \bar{36.3} \end{matrix}$
Zeta-Image	6.9	5.0	-	-
Gamut Mapping	6.9	4.9	5.2	37.1
Bayesian	6.7	4.7	$\begin{matrix} \bar{5.0} \end{matrix}$	39.4
Weighted Grey-Edge	6.6	4.7	5.1	44.3
Exemplar-Based	$\begin{matrix} \bar{5.2} \end{matrix}$	$\begin{matrix} \bar{3.7} \end{matrix}$	-	-
Proposed algorithm	5.1	3.4	3.8	28.5

Table 2. Angular errors for original ColorChecker dataset for different illumination estimation algorithms(°)

Method	Mean	Median	Trimean	Max
Do nothing	13.7	13.6	13.5	27.4
Grey-World	6.4	6.3	6.3	24.8
White-Patch	7.6	5.7	6.4	40.6
Shades-of-Grey	4.9	4.0	4.2	22.4
Grey-Edge	5.1	4.4	4.6	23.9
Zeta-Image	4.1	2.8	-	-
Gamut Mapping	4.2	2.3	2.9	23.2
Bayesian	4.8	3.5	3.9	24.5
Multi-Cue	3.3	2.2	2.6	-
Deep-CC	$\begin{matrix} \bar{2.6} \end{matrix}$	$\begin{matrix} \bar{2.0} \end{matrix}$	$\begin{matrix} \bar{2.1} \end{matrix}$	14.8
Exemplar-Based	2.9	2.3	2.4	19.4
Proposed algorithm	2.5	1.8	2.0	$\begin{matrix} \bar{17.9} \end{matrix}$

Table 3. Angular errors for re-processing of ColorChecker dataset for different illumination estimation algorithms(°)

Method	Mean	Median	Trimean	Max
Do nothing	8.3	6.7	7.3	36.8
Grey-World	7.9	7.0	7.1	48.1
White-Patch	6.8	5.3	5.8	$\begin{matrix} \bar{38.7} \end{matrix}$
Shades-of-Grey	6.1	5.3	5.5	41.2
Grey-Edge	5.9	4.7	5.1	41.2
Gamut Mapping	7.1	5.8	6.1	41.9
Multi-Cue	8.8	5.6	6.8	-
Exemplar-Based	$\begin{matrix} \bar{4.4} \end{matrix}$	$\begin{matrix} \bar{3.4} \end{matrix}$	$\begin{matrix} \bar{3.7} \end{matrix}$	45.6
Proposed algorithm	4.2	3.0	3.4	43.0

Table 4. Angular errors for SFU Grey-ball dataset for different illumination estimation algorithms(°)

Method	Number of Illuminants
Method	One	Two	Multi
Grey-World	8.9	6.4	-
White-Patch	7.8	6.7	-
Grey-Edge (n=1)	6.4	5.6	-
Grey-Edge (n=2)	$\begin{matrix} \bar{5.0} \end{matrix}$	5.1	-
Exemplar-Based	5.1	$\begin{matrix} \bar{3.8} \end{matrix}$	$\begin{matrix} \bar{4.3} \end{matrix}$
Proposed algorithm	4.2	3.6	3.7

Table 5. Median angular errors for multiple light sources dataset for different illumination estimation algorithms(°)

Dataset	Without segmentation				With segmentation
Dataset	Mean	Median	Trimean	Max	Mean	Median	Trimean	Max
Original ColorChecker	5.3	3.7	4.0	32.5	5.1	3.4	3.8	28.5
Re-processing of ColorChecker	2.6	2.0	2.1	18.6	2.5	1.8	2.0	17.9
SFU Grey-ball	4.4	3.2	3.5	45.9	4.2	3.0	3.4	43.0

Table 6. Angular errors for the proposed method with and without segmentation(°)

Dataset	Without segmentation	With segmentation
Dataset	Estimation	Segmentation+Estimation
Original ColorChecker	0.9	6.3+89.3
Re-processing of ColorChecker	1.2	41.1+183.8
SFU Grey-ball	23.2	1.0+164.3

Table 7. Average consuming time for the proposed method with and without segmentations

Shuai Cui, Jun Zhang, Jun Gao. Illumination Estimation Based on Exemplar Learning in Logarithm Domain[J]. Acta Optica Sinica, 2018, 38(2): 0233001

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