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Comp 790 Computational Photography Spatially Varying White Balance Megha Pandey Sept 16 2008 Color Constancy Color Constancy interpretation of material colors independent of surrounding illumination Color Constancy Color Constancy interpretation of material colors independent of surrounding illumination Digital Images under Varying Illumination Cameras can not adapt to varying illumination as humans do images have a color cast depending on the light source Cameras can not adapt to varying illumination as humans do images have a color cast depending on the light source Color Temperature Color temperature of a light source is the temperature of an ideal black body radiator at which the color of the color of the light source and the black body are identical Incandescent Light Orange Color Cast Moonlight Blue Color Cast Fluorescent Light Green Color Cast Color Balance Color Balance adjusting the color components to eliminate color casts Chromatic Adaptation estimation of representation of object as it would appear under a different light source than the one in which it was recorded White Balance aims to render neutral colors correctly to emulate the property of color constancy Color Balance adjusting the color components to eliminate color casts White Balance aims to render neutral casts correctly to render visually pleasing images white balanced image White Balance Tools Digital Cameras Auto White Balance White Balance Caps Gray Cards Take a picture of a neutral object white or gray Deduce the weight of each channel If the object is recoded as R w G w B w use weights 1 R w 1 G w 1 B w Auto WB Custom WB Color Correction Filters Mixed Lighting Light Filters Gel Filters Light Filters White Balance under Mixed Lighting Barnard 1997 adaptation of gamut based color constancy technique Assumes smooth illumination Kawakami 2005 outdoor scenes with hard shadows illuminants restricted to black body radiators Lischinski 2006 user scribbles correct localized color casts Ebner 2004 local color shifts Gray World Assumption Local Color Shift Light Mixture Estimation for Spatially Varying White Balance Eugene Hsu Tom Mertens Sylvain Paris Shai Avidan Fredo Durand Several slides from Eugene Hsu Algorithm Overview Recovers the dominant material colors and uses them to estimate the relative proportion of the two light colors at each of the pixels Input image illuminated by two light types Voting scheme to recover dominant material colors in the scene Estimate light mixture at reliable pixels and interpolate missing values Estimated light mixture is used to achieve spatially varying white balance Assumptions Two light sources specified by the user Interaction of light can be described using RGB channels only Surfaces are Lambertian and non fluorescent which implies that the image color is the product of illumination and reflectance Color bleeding due to indirect illumination can be ignored Image Formation Model Observed pixel color is material color multiplied by scaled light color White Balance Proper white balance is achieved by inverting the effect of the light source color Proper white balance is achieved by inverting the effect of the light source color Image model with two light sources Proper white balance can be achieved if the relative proportion of the two light sources is known Solving for is under constrained since the actual material colors are not given Material Color Estimation Assume scene is dominated by a small set of material colors hence reflectance spectra is sparse Material Color Estimation Assume scene is dominated by a small set of material colors hence reflectance spectra is sparse Material Color Estimation Assume scene is dominated by a small set of material colors hence reflectance spectra is sparse Scene viewed in white light Material Color Estimation Assume scene is dominated by a small set of material colors hence reflectance spectra is sparse Scene viewed in mixed light Sample material colors and find the one that accounts for the observed color of most pixels Given a candidate material color a pixel votes for a material color only if the observed color can be explained by a combination of given light sources If this expression holds we say that the pixel votes for the material color 48 48 16 Light mixture estimation for reliable pixels Mixture Interpolation Assume L1B and L2B are 1 divide out the blue channels This looks exactly like Image Matting Interpolation is performed using Matting Laplacian Levin et al 2006 Scene shot with multiple exposures so that ground truth is available Constraint the marked points and interpolate the rest Smooth interpolation is pretty bad Edge aware interpolation doesn t work satisfactorily either Matting Laplacian gives much better result Experiments Synthetic Data Input Output Input Ground Truth Output Comparison Experiments Real Data Input Alpha Map Output Input Output Input LME Local Color Shift LME Local Color Shift Scene Relighting Separate the two lighting contributions from the white Balanced image The observed scene is a blend of two images as seen by either of the light sources in proportions and 1 Multiply the white balanced image by for the first contribution Multiply the white balanced image by for the first Contribution and by 1 for the second contribution We can choose new lights and add desired effects Input Output Discussion Works best for raw image data Better results for indoor scenes Handles specularities and inter reflections Material colors should exhibit enough color variation for the voting to work Accurate specification of light sources is required Scalability Issues


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UNC-Chapel Hill COMP 790 - Spatially Varying White Balance

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