a color balancing algorithm for cameras - stacks
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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
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ΓΓΓ
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BBBGBR
GBGGGR
RBRGRR
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� Digital cameras use fast color balancing
� Demosaicing a non-balanced image
is sub-optimal
White illumination Blue illumination [0.4 0.6 1]
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BGB
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BGRbgr =
� Digital cameras use fast color balancing
algorithms to estimate illumination
� Image segmentation, feature detection and � Proposed color balancing
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Histogram of unique RGBs
� Parameterize
ellipsoid
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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
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≤+∈= 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]