automatic texture guided color transfer and colorization...automatic texture guided color transfer...
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Automatic Texture Guided Color Transfer and Colorization
B. Arbelot1, R. Vergne1, T. Hurtut2 & J. Thollot1
1 Inria-LJK (UGA, CNRS), France2 Polytechnique Montréal, Canada
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Color Transfer
Input & Reference Our Result
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Colorization
Input & Reference Our Result
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Applications
● Photography / Cinema – Colors = mood, atmosphere, emotion
© Sweeney Todd
© The Matrix © Le Fabuleux Destin d'Amelie Poulain
© The Grand Budapest Hotel
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Applications
● Visualization / Imaging– Colors = information
© livescience.com© H. Zaidi
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Previous Work
● Global histogram transfers[Reinhard01], [Xiao06], [Pitié07]
[Reinhard01] Input Reference Result
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Previous Work
● Global histogram transfers[Reinhard01], [Xiao06], [Pitié07]
++ Simple, fast
++ Work well on similar images
[Reinhard01] Input Reference Result
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Previous Work
● Global histogram transfers[Reinhard01], [Xiao06], [Pitié07]
- - Unreliable
[Xiao 2006] Input Reference Result
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Previous Work
● Global histogram transfers[Reinhard01], [Xiao06], [Pitié07]
- - Unreliable
- - Can create new colors
Input Reference Result [Xiao 2006]
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Previous Work
● Local Transfers
Images from [Hristova15]
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Previous Work
● Local Transfers[Tai05], [Bonneel11], [Hristova15]
++ More accurate than global transfers
[Hristova15] Input Reference Result
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Previous Work
● Local Transfers[Tai05], [Bonneel11], [Hristova15]
++ More accurate than global transfers
- - Fail to distinguish between textured regions
[Hristova15] Input Reference Result
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Colorization
● No source color histogram
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Colorization
● No source color histogram– Transfer is impossible
– Chrominance need to be added
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Colorization
● Optimization based stroke diffusion [Levin04]
Input + user strokes Result
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Colorization
● Exemplar based approaches– Use a reference image [Welsh02], [Charpiat08],
[Gupta12], [Bugeau14]
Input Reference Result
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Colorization
● Exemplar based approaches
Images from [Gupta12]
Reference
Input
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Colorization
● Exemplar based approaches
Images from [Gupta12]
Features extraction
Reference
Input
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Colorization
● Exemplar based approaches
Images from [Gupta12]
Features extraction
Feature matching Reference
Input
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Colorization
● Exemplar based approaches
Images from [Gupta12]
Features extraction
Feature matching
Result
Reference
Input
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Colorization
● Exemplar based approaches
++ Very few user input
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Colorization
● Exemplar based approaches
++ Very few user input
- - Color coherency is hard to preserve
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Our Approach
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Our Approach
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Our Approach
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Our Approach
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Descriptors
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Descriptors
● How to describe a texture ?
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Descriptors
● How to describe a texture ?
Through the 1st and 2nd order luminance variations [Julesz70-80]
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Descriptors
● How to describe a texture ?
Through the 1st and 2nd order luminance variations [Julesz70-80] – We chose the covariance matrices introduced by
[Tuzel06] as 2nd order descriptors
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Descriptors
● How to describe a texture ?
Through the 1st and 2nd order luminance variations [Julesz70-80] – We chose the covariance matrices introduced by
[Tuzel06] as 2nd order descriptors
– Enriched by 1st order means similar to [Karacan13]
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Descriptors
● How to describe a texture ?
Through the 1st and 2nd order luminance variations [Julesz70-80] – We chose the covariance matrices introduced by
[Tuzel06] as 2nd order descriptors
– Enriched by 1st order means similar to [Karacan13]
– Computed on the luminance and its derivatives
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Descriptors
● Our feature vector
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Descriptors
● Our feature vector
Luminance
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Descriptors
● Our feature vector
First derivatives of theluminance
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Descriptors
● Our feature vector
Second derivativesof the luminance
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Descriptors
● Our descriptor
S(p) =Covariance matrix
Information(6x6)
Second orderInformation
Meanof
features(6x1)
First orderInformation
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Descriptors
● Descriptor for each pixel
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Descriptors
● Descriptor for each pixel
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Descriptors
● Descriptor for each pixel
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Descriptors
● Descriptor for each pixel
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Descriptors
● Descriptor for each pixel
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Descriptors
● Descriptor for each pixel
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Descriptors
● Descriptor for each pixel
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Descriptors
● Descriptor for each pixel
Descriptors
● Descriptor for each pixel
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Descriptors
● Descriptor for each pixel
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Descriptors
● Descriptor for each pixel
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Descriptors
● Descriptor for each pixel
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Descriptors
● Descriptor for each pixel
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Descriptors
● Descriptor window size
Input window 13x13 window 43x43(512x512)
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Descriptors
● We want homogeneous descriptors over textured regions
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Descriptors
● We want homogeneous descriptors over textured regions➔ Compute them with a large window
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Descriptors
● We want homogeneous descriptors over textured regions➔ Compute them with a large window
● We want to preserve image structure and texture transitions
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Descriptors
● We want homogeneous descriptors over textured regions➔ Compute them with a large window
● We want to preserve image structure and texture transitions➔ Adapt descriptors around those transitions
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Descriptors
● We want homogeneous descriptors over textured regions➔ Compute them with a large window
● We want to preserve image structure and texture transitions➔ Adapt descriptors around those transitions➔ The descriptors variance allows to detect those
transition areas !
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Descriptors
● Gradient descent guided by the descriptors variance
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Descriptors
● Gradient descent guided by the descriptors variance
Input
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Descriptors
● Gradient descent guided by the descriptors variance
Input Descriptors
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Descriptors
● Gradient descent guided by the descriptors variance
Input Descriptors Variance
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Descriptors
● Gradient descent guided by the descriptors variance
Input Descriptors Variance
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Descriptors
● Gradient descent guided by the descriptors variance
Input Descriptors Variance
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Descriptors
● Gradient descent guided by the descriptors variance
Input Descriptors Variance
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Descriptors
● Gradient descent guided by the descriptors variance
Input Descriptors Variance
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Descriptors
● Gradient descent guided by the descriptors variance
Input Descriptors Variance Descriptors aftergradient descent
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Descriptors
● Multiscale gradient descent
Descriptors
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Descriptors
● Multiscale gradient descent
Descriptors Standard gradientdescent
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Descriptors
● Multiscale gradient descent
Descriptors Standard gradientdescent
Multiscale gradient descent
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Descriptors
● Multiscale gradient descent
Descriptors Standard gradientdescent
Multiscale gradient descent
!!!
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Descriptors
● Unnormalized bilateral filter [Aubry14]
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Descriptors
● Unnormalized bilateral filter [Aubry14]– Guided by the input image luminance
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Descriptors
● Unnormalized bilateral filter [Aubry14]– Guided by the input image luminance
– Little to no halos, no gradient reversal
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Descriptors
● Unnormalized bilateral filter [Aubry14]– Guided by the input image luminance
– Little to no halos, no gradient reversal
– Similar to an anisotropic diffusion
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Descriptors
● Unnormalized bilateral filter [Aubry14]
Input Descriptors aftergradient descent
Descriptors aftergradient descent
and filtering
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Similarity Maps
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Similarity Maps
● Similarity computed as the L2 Euclidean distance between two descriptors, filtered by a Gaussian
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Similarity Maps
● Similarity computed as the L2 Euclidean distance between two descriptors, filtered by a Gaussian
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Similarity Maps
● Similarity computed as the L2 Euclidean distance between two descriptors, filtered by a Gaussian
Control parameter
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Similarity Maps
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Similarity Maps
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Similarity Maps
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Color Transfer
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Color Transfer
● Global transfer from [Reinhard01]
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Color Transfer
● Global transfer from [Reinhard01]– Transfer the mean and standard deviation of each
channel of the reference to the input
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Color Transfer
● Global transfer from [Reinhard01]– Transfer the mean and standard deviation of each
channel of the reference to the input
– Translate and stretch the input histogram
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Color Transfer
● Global transfer from [Reinhard01]– Transfer the mean and standard deviation of each
channel of the reference to the input
– Translate and stretch the input histogram
● We use weighted means and standard deviations using our similarity maps
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Color Transfer
● Global transfer from [Reinhard01]– Transfer the mean and standard deviation of each
channel of the reference to the input
– Translate and stretch the input histogram
● We use weighted means and standard deviations using our similarity maps➔ Perform a local transfer between similar pixels
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Color Transfer
● Our Result
Input & Reference Result with σd= 1
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Color Transfer
● Effect of σd
Input & Reference Result with σd= 2
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Color Transfer
● Effect of σd
Input & Reference Result with σd= 4
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Color Transfer
● Effect of σd
= [Reinhard01]
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Color Transfer
Input Reference Our Result
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Transfer - Comparisons
Input Reference
Transfer - Comparisons
Input Reference [Reinhard01] [Xiao06]
[Pitié07] [Pouli11] [Hristova15] Our Result
Transfer - Comparisons
Input Reference [Reinhard01] [Xiao06]
[Pitié07] [Pouli11] [Hristova15] Our Result
Transfer - Comparisons
Input Reference [Reinhard01] [Xiao06]
[Pitié07] [Pouli11] [Hristova15] Our Result
Transfer - Comparisons
Input Reference [Reinhard01] [Xiao06]
[Pitié07] [Pouli11] [Hristova15] Our Result
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Colorization
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Colorization
● We cannot transfer missing colors
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Colorization
● We cannot transfer missing colors● Each output pixel is given the mean
chromaticity of the reference
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Colorization
● We cannot transfer missing colors● Each output pixel is given the mean
chromaticity of the reference, weighted by the similarity maps
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Colorization
● Result
Input & Reference Result with σd= 0.2
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Colorization
● Effect of σd
Input & Reference Result with σd= 1
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Colorization
● Effect of σd
Input & Reference Result with σd= 2
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Colorization
Input Reference Our Result
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Colorization - Comparisons
Input Reference
Colorization - Comparisons
Input Reference [Welsh02] [Charpiat08]
[Gupta12] [Bugeau14] [Pierre15] Our Result
Colorization - Comparisons
Input Reference [Welsh02] [Charpiat08]
[Gupta12] [Bugeau14] [Pierre15] Our Result
Colorization - Comparisons
Input Reference [Welsh02] [Charpiat08]
[Gupta12] [Bugeau14] [Pierre15] Our Result
Colorization - Comparisons
Input Reference [Welsh02] [Charpiat08]
[Gupta12] [Bugeau14] [Pierre15] Our Result
Colorization - Comparisons
Input Reference [Welsh02] [Charpiat08]
[Gupta12] [Bugeau14] [Pierre15] Our Result
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Limitations
● Texture based approach
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Limitations
● Texture based approach– Not well suited for texture-less images
● Similar to applying a global transfer
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Limitations
● Texture based approach– Not well suited for texture-less images
– Fine structures can be mistaken as textures
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Limitations
● Texture based approach– Not well suited for texture-less images
– Fine structures can be mistaken as textures
Input Reference Our Result
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Limitations
● Texture based approach– Not well suited for texture-less images
– Fine structures can be mistaken as textures
Input Reference Our Result
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Future Works
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Future Works
● Edge preserving texture descriptors
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Future Works
● Edge preserving texture descriptors– Can be used in a lot of different applications
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Future Works
● Edge preserving texture descriptors– Can be used in a lot of different applications
● Transfer and colorization
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Future Works
● Edge preserving texture descriptors– Can be used in a lot of different applications
● Transfer and colorization– Several references could be used
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Future Works
● Edge preserving texture descriptors– Can be used in a lot of different applications
● Transfer and colorization– Several references could be used
– Could be extended to video inputs
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Thank you for your attention
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Descriptors
● Effect of each step on a transfer result
Initial descriptors Only grad. descent Only filtering Both