A Color Balancing Algorithm for Cameras - Stacks
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A Color Balancing Algorithm for Cameras A Color Balancing Algorithm for Cameras Noy Cohen Noy Cohen Department of Electrical Engineering, Stanford University Background and Motivation Color Balancing in Cameras Background and Motivation Color Balancing in Cameras The human visual system is largely color Color Balancing as Part of Color Balancing Algorithm Description constant, however cameras are not the Camera’s ISP Pipeline Two main correction methods Γ R R ' 0 0 Isolate pixel candidates for gray Extract unique colors Max/min constraints Luminance- weighted average Γ Γ Γ Γ Γ Γ Γ Γ Γ = Γ Γ Γ = B G R B G R B G R B G R BB BG BR GB GG GR RB RG RR B G R ' ' ' ' ' ' 0 0 0 0 0 0 average ∑ = = K 1 i i i R G R ˆ Digital cameras use fast color balancing Demosaicing a non-balanced image is sub-optimal White illumination Blue illumination [0.4 0.6 1] ∑ ∑ = = = = K 1 i i i K 1 i 2 i B G B ˆ G G ˆ [ ] [ ] [ ] B ˆ G ˆ R ˆ b ˆ g ˆ r ˆ = algorithms to estimate illumination Image segmentation, feature detection and Proposed color balancing 140 140 140 140 140 140 140 140 140 140 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 56 84 56 84 56 56 84 56 84 56 48 80 48 80 48 48 80 48 80 48 48 80 48 80 48 Histogram of unique RGBs Parameterize ellipsoid [ ] { } 1 1 0 2 2 ≤ + ∈ = b Ap p K ε [ ] [ ] [ ] B ˆ G ˆ R ˆ B G R b ˆ g ˆ r ˆ = object recognition can benefit from improved color accuracy Proposed color balancing RAW Processing Blocks Diagonal Color Correction Sensor Demosaicing Linear Color Correction RGB Processing Blocks Illumination Estimation Neutral Find A, b Extract points 2 N 1 i 1 subject to det log minimize 2 1 K = ≤ + - b Ap A i Related Work Experimental Results improved color accuracy Related Work Experimental Results J. Van de Weijer, T. Gevers and A. Gijsenji, “Edge-Based Color Constancy,” IEEE Trans. on Image Proc., vol. 16, no. 9, September 2007 Mean Median Max Std Input image Ground truth Gray world IEEE Trans. on Image Proc., vol. 16, no. 9, September 2007 F. Ciurea and B. Funt, “A Large Image Database for Color Constancy Research,” Proceedings of the IS&T Eleventh Color Imaging Conference, pp. 160-164, Scottsdale, November 2003 Gray-world 7.3° 6.28° 42.63° 4.68° Max-RGB 7.86° 6.22° 27.41° 6.69° Shades of 6.67° 5.83° 35.66° 4.52° pp. 160-164, Scottsdale, November 2003 G. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” Proc. IS&T/SID Twelfth Color Imaging Conference, pg. 37, 2004 G. Finlayson, S. Hordley, and P. Hubel, “Color by correlation: A simple, Shades of gray 6.67° 5.83° 35.66° 4.52° Gray-edge 6.27° 5.32° 34.02° 4.44° Max-edge 8.39° 6.72° 36.32° 6.24° ⋅ l l ˆ Max-RGB Shades of gray Gray-edge unifying framework for color constancy,” IEEE Trans. Pattern Anal. Machine Intell., vol. 23, pp. 1209–1221, 2001 Finlayson et al, 2001 Van de Weijer et al, 2007 Ciurea et al, 2003 Max-edge 8.39° 6.72° 36.32° 6.24° Color by correlation 10.24° 7.86° 38.87° 9.03° Proposed 5.5° 4.47° 30.51° 4.02° ⋅ = gt gt 1 - l l ˆ l l ˆ cos E Finlayson et al, 2001 Van de Weijer et al, 2007 Ciurea et al, 2003 Proposed 5.5° 4.47° 30.51° 4.02° Improved performance Max-edge Color by correlation Proposed Complexity O(n) Average runtime 0.4 [ms] Average runtime 0.4 [ms]
Transcript of A Color Balancing Algorithm for Cameras - Stacks
A Color Balancing Algorithm for CamerasA Color Balancing Algorithm for CamerasNoy CohenNoy Cohen
Department of Electrical Engineering, Stanford University
Background and Motivation Color Balancing in CamerasBackground and Motivation Color Balancing in Cameras
� The human visual system is largely color Color Balancing as Part of Color Balancing Algorithm Description� The human visual system is largely color
constant, however cameras are not
Color Balancing as Part of
the Camera’s ISP Pipeline� Two main correction methods
Γ RR' 00
Color Balancing Algorithm Description
Isolate pixel
candidates for gray
Extract unique
colors
Max/min
constraints
Luminance-
weighted
average
ΓΓΓ
ΓΓΓ
ΓΓΓ
=
Γ
Γ
Γ
=
B
G
R
B
G
R
B
G
R
B
G
R
BBBGBR
GBGGGR
RBRGRR
B
G
R
'
'
'
'
'
'
00
00
00 average
∑=
=K
1i
iiRGR
� Digital cameras use fast color balancing
� Demosaicing a non-balanced image
is sub-optimal
White illumination Blue illumination [0.4 0.6 1]
∑
∑
=
=
=
=
K
1i
ii
K
1i
2
i
1i
BGB
GG
[ ] [ ][ ]
BGRbgr =
� Digital cameras use fast color balancing
algorithms to estimate illumination
� Image segmentation, feature detection and � Proposed color balancing
140
140
140
140
140
140
140
140
140
140
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
56
84
56
84
56
56
84
56
84
56
48
80
48
80
48
48
80
48
80
48
48
80
48
80
48
Histogram of unique RGBs
� Parameterize
ellipsoid
[ ]{ }1102
2≤+∈= bApp Kε
[ ] [ ][ ]BGR
BGRbgr =
� Image segmentation, feature detection and
object recognition can benefit from
improved color accuracy
� Proposed color balancing
RAW
Processing
Blocks
Diagonal
Color
CorrectionSensor
Demosaicing
Linear
Color
Correction
RGB
Processing
Blocks
Illumination
Estimation
Neutral � Find A, b
� Extract points
[ ]{ }1102
≤+∈= bAppε
N1i 1subject to
detlogminimize
2
1
K=≤+
−
bAp
A
i
Related Work Experimental Results
improved color accuracy
Related Work Experimental Results� J. Van de Weijer, T. Gevers and A. Gijsenji, “Edge-Based Color Constancy,”
IEEE Trans. on Image Proc., vol. 16, no. 9, September 2007Mean Median Max Std Input image Ground truth Gray world
IEEE Trans. on Image Proc., vol. 16, no. 9, September 2007
� F. Ciurea and B. Funt, “A Large Image Database for Color Constancy
Research,” Proceedings of the IS&T Eleventh Color Imaging Conference,
pp. 160-164, Scottsdale, November 2003
Gray-world 7.3° 6.28° 42.63° 4.68°
Max-RGB 7.86° 6.22° 27.41° 6.69°
Shades of 6.67° 5.83° 35.66° 4.52°pp. 160-164, Scottsdale, November 2003
� G. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” Proc.
IS&T/SID Twelfth Color Imaging Conference, pg. 37, 2004
� G. Finlayson, S. Hordley, and P. Hubel, “Color by correlation: A simple,
Shades of
gray
6.67° 5.83° 35.66° 4.52°
Gray-edge 6.27° 5.32° 34.02° 4.44°
Max-edge 8.39° 6.72° 36.32° 6.24° ⋅ll
Max-RGB Shades of gray Gray-edge
unifying framework for color constancy,” IEEE Trans. Pattern Anal. Machine
Intell., vol. 23, pp. 1209–1221, 2001
Finlayson et al, 2001Van de Weijer et al, 2007Ciurea et al, 2003
Max-edge 8.39° 6.72° 36.32° 6.24°
Color by
correlation
10.24° 7.86° 38.87° 9.03°
Proposed 5.5° 4.47° 30.51° 4.02°
⋅=
gt
gt1-
ll
llcosE
Finlayson et al, 2001Van de Weijer et al, 2007Ciurea et al, 2003 Proposed 5.5° 4.47° 30.51° 4.02°
� Improved performance Max-edge Color by correlation Proposed
� Complexity O(n)
� Average runtime 0.4 [ms]� Average runtime 0.4 [ms]
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