CN102521807A - Method for transferring colors by utilizing color space distribution - Google Patents

Method for transferring colors by utilizing color space distribution Download PDF

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CN102521807A
CN102521807A CN2011103961721A CN201110396172A CN102521807A CN 102521807 A CN102521807 A CN 102521807A CN 2011103961721 A CN2011103961721 A CN 2011103961721A CN 201110396172 A CN201110396172 A CN 201110396172A CN 102521807 A CN102521807 A CN 102521807A
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董未名
吴富章
张晓鹏
梅星
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Institute of Automation of Chinese Academy of Science
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Abstract

The invention discloses a method for transferring colors by utilizing color space distribution. The method comprises the following steps of: by combination of color and space position information of image pixels, segmenting a source image and a target image into a plurality of dominant color areas; carrying out calculation on each dominant color area in the source image and the target image and obtaining the characteristic of each dominant color area; using a color mean and geometric center information of the dominant color areas to establish an optimal many-to-many probability map between the dominant color areas of the source image and the dominant color areas of the target image and leading the sum of the mapping cost among the dominant color areas to be minimum; and according to the optimal probability map, carrying out calculation on the dominant color areas of the source image again to obtain a new color mean and a new color variance, and updating the color of each pixel in the dominant color areas of the source image.

Description

The method of utilizing color space to distribute color is transmitted
Technical field
The invention belongs to the digital image processing techniques field, a kind of target image that utilizes that relates to computer graphics and Digital Image Processing is edited the image processing method of particularly picture tone being edited and being strengthened as a reference to image.
Background technology
Along with a large amount of uses of image acquisition equipments such as digital camera, digital picture becomes very general media information.Because odjective causes such as capture environment, camera parameters, the original image that obtains does not often satisfy people's expectation, therefore need carry out post-processed by the graph and image processing technology.The image manipulation that some are basic like denoising, convergent-divergent, cutting, adjustment luminance contrast etc., can be handled by the image processing software (Photoshop etc.) of specialty.Yet when utilizing the color harmony Color Style of conventional image processing software adjustment image, the layman often is difficult to reach the effect of wanting.
The color of image transmission is a kind of picture editting's technology easily and effectively.Reinhard (REINHARD, E., ASHIKHMIN; M., GOOCH, B.; AND SHIRLEY, P.2001.Color transfer between images.IEEE Comput.Graph.Appl.21 (September) 34-41.) has at first proposed global image color transmission method.This method can be delivered to the dominant color style of piece image in another width of cloth image, thereby reaches the purpose of automatic adjustment picture tone.The computing of global color transmission method is simple, but only when source images and target image have similar distribution of color, just can obtain effect preferably.Dong (DONG, W., BAO, G.; ZHANG, X., AND PAUL, J.-C.2010.Fast local color transfer via dominant colors mapping.In ACM SIGGRAPH ASIA 2010Sketches; ACM, New York, NY, USA; SA ' 10,46:1-46:2.) utilize the method for extracting dominant color, in source images and target image, extract the dominant color of equal number, and between source images and target image dominant color, make up the mapping one to one of an optimum.When source images and target image had the dominant color of equal number, this method can guarantee that all dominant color of target image all pass to source images.If but the dominant color quantity of source images and target image is when inconsistent, the method for Dong can not obtain a gratifying result usually.
Goal of the invention
Being directed to source images dominant color quantity is less than under the situation of target image dominant color quantity; The dominant color of target image can not pass to this problem of source images fully; The invention provides all dominant color that can guarantee target image and all be delivered in the source images, and these target image dominant color can also keep a kind of method of utilizing color space to distribute color is transmitted of its original locus distribution in source images.
For realizing above-mentioned purpose, a kind of method of utilizing color space to distribute color is transmitted provided by the invention comprises that step is following:
Step S1: combining image color of pixel and spatial positional information, utilize the average drifting algorithm that source images and target image are divided into a plurality of dominant color zone;
Step S2: regard the pixel in the dominant color zone as a sample set; Calculate in each dominant color zone in source images, the target image, obtains the following characteristic in each dominant color zone: all pixels that color average, color variance, geometric center and this dominant color zone comprise;
Step S3: color average and the geometric center information of utilizing the dominant color zone; Between source images dominant color zone and target image dominant color zone, set up the probability mapping of a multi-to-multi; Each dominant color zone of source images is regional with each dominant color that different probability is mapped to target image, ask for source images dominant color zone and make the mapping cost summation between the dominant color zone minimum to probability mapping optimum between the target image dominant color zone;
Step S4: according to the probability mapping of optimum, again source images dominant color zone is calculated, obtain new color average and the color variance in source images dominant color zone;
Step S5: utilize new color average and color variance, each color of pixel in the source images dominant color zone is upgraded.
Beneficial effect of the present invention: the present invention utilizes average drifting technology (Mean Shift) that image is cut apart, and obtains the dominant color zone of image; The probability that adopts earthquake distance method (Earth Mover ' s Distance) between source images dominant color zone and target image dominant color zone, to set up a multi-to-multi then shines upon, and lets each dominant color zone is mapped in each target dominant color zone with the most appropriate probability in the source images; Because in two processes of image segmentation and foundation mapping, considered that all the locus of color distributes, so the dominant color of target image will be delivered in the source images together with its locus simultaneously; Even the dominant color quantity of source images and target image is inconsistent, can guarantee that also colors all in the target image all is applied to source images; Whole process of the present invention is automatic fully, and under target image and source images dominant color quantity differ bigger situation, also can obtain result preferably.This method can not only pass to source images with the tone style of target image, and the locus that can also make the result images that obtains keep color of object distributes.
Description of drawings
Fig. 1 is the frame diagram of the inventive method;
Fig. 2 is an algorithm workflow synoptic diagram;
Fig. 3 is a dominant color zone mapping synoptic diagram;
Fig. 4 is dominant color region quantity inconsistent results figure;
Fig. 5 is based on the semantic color transmission of part figure as a result.
Embodiment
Specify each related detailed problem in the technical scheme of the present invention below in conjunction with accompanying drawing.Be to be noted that described embodiment only is intended to be convenient to understanding of the present invention, and it is not played any qualification effect.
As depicted in figs. 1 and 2, method of the present invention mainly is divided into three steps: image segmentation; The mapping of dominant color zone; The source images color is upgraded.Adopt average drifting (Mean Shift) image partition method that image is cut apart, make adjacent and have similar color pixel and make as a whole participation color map; Between all source images dominant color zones and target image dominant color zone, set up the probability mapping of a multi-to-multi; The cost summation that color is transmitted is minimum, thereby each the dominant color zone in the source images all is mapped to several the most similar with it in target image dominant color zones; For some dominant color zone, utilize this dominant color zone and come to upgrade simultaneously all color of pixel in this dominant color zone in abutting connection with the mapping result in dominant color zone, thus the border between the smooth region.Said dominant color zone is the contiguous pixels zone that has similar color attribute in source images or the target image.
To specifying below the specific algorithm of each step.
For the locus that keeps color distributes, need when split image, consider the space continuity of color.Utilize Mean Shift partitioning algorithm (COMANICIU in the method for the present invention; D.; AND MEER, P.2002.Mean shift:A robust approach toward feature space analysis.IEEE Trans.Pattern Anal.Mach.Intell.24 (May)) source images and target image are cut apart.Mean Shift algorithm is combined into the vector of one five dimension (coloured image) or three-dimensional (gray level image) with color of pixel and spatial positional information, and utilizes the non-parametric estmation method that the probability density of vector is estimated.All be regarded as a cluster centre to each extreme point of probability density.Can image segmentation be become several zones according to these cluster centres, be referred to as the dominant color zone.To each dominant color zone, calculate the set of its all pixels compositions of color average, color variance, geometric center coordinate and intra-zone, and these information are used for representing this dominant color zone as the regional characteristic of dominant color.
The characteristic in calculation sources image dominant color zone comprises as follows:
Figure BDA0000115672340000042
Figure BDA0000115672340000043
Figure BDA0000115672340000044
i=1,2,....,M s
In the formula, subscript s representes source images;
Figure BDA0000115672340000045
The set that all pixels are formed in i dominant color zone of expression source images, The geometric center in i dominant color zone of expression source images,
Figure BDA0000115672340000047
The sum of all pixels in i dominant color zone of expression source images, I s(x, y) expression be positioned in the source images position (x, the pixel color of y) locating, The color average in i dominant color zone of expression source images,
Figure BDA0000115672340000049
The color variance in i dominant color zone of expression source images,
Figure BDA00001156723400000410
I dominant color zone of expression source images, M sThe dominant color region quantity of expression source images.
The characteristic of calculating target image dominant color zone comprises as follows:
Figure BDA00001156723400000411
Figure BDA0000115672340000052
j=1,2,....,M t
Subscript t representes target image in the formula;
Figure BDA0000115672340000054
The set that all pixels are formed in j dominant color zone of expression target image,
Figure BDA0000115672340000055
The geometric center in j dominant color zone of expression target image,
Figure BDA0000115672340000056
The sum of all pixels in j dominant color zone of expression target image, I t(x, y) expression be positioned in the target image position (x, the pixel color of y) locating, The color average in j dominant color zone of expression target image,
Figure BDA0000115672340000058
The color variance in j dominant color zone of expression target image, J dominant color zone of expression target image; M tThe dominant color region quantity of expression target image.
Mapping synoptic diagram in dominant color zone as shown in Figure 3; In order between source images dominant color zone and target image dominant color zone, to set up mapping, the present invention utilizes earthquake distance (EMD) method (RUBNER, Y.; TOMASI; C, AND GUIBAS, L.J.2000.The earth mover ' s distance as a metric for image retrieval.Int.J.Comput.Vision 40; 2,99-121.) the multi-to-multi probability mapping of the best of structure between two dominant color regional ensembles.Asking for source images dominant color zone comprises the minimum step of mapping cost summation between the dominant color zone to probability mapping optimum between the target image dominant color zone:
If it is w that i dominant color zone of source images is mapped to the probability in j dominant color zone of target image Ij, the mapping cost between the dominant color zone is l Ij, then corresponding cost summation is:
cos t = Σ i M s Σ j M t w ij l ij ,
M sAnd M tBe respectively the quantity in source images and target image dominant color zone.In this formula, mapping probability w IjNeed satisfy following constraint:
Σ i M s w ij = 1 M t , j=1,2,...,M t
Σ j M t w ij = 1 M s , i=1,2,...,M s
M sBe the dominant color region quantity of source images, M tDominant color region quantity for target image.
Finding the solution optimum probability
Figure BDA00001156723400000513
through earthquake distance algorithm (Earth Mover ' s Distance) makes mapping cost summation cost minimum:
Σ i M s Σ j M t w ij * d ij = min w ij Σ i M s Σ j M t w ij d ij ,
For the space distribution of target image color also can be delivered in the source images, method of the present invention is at structure mapping cost function l IjIn time, be integrated into the color distortion between the dominant color zone and differences in spatial location in the mapping cost simultaneously.This mapping cost function defines as follows:
l ij = e ( | | c i s - c t j | | ϵ g ) * e ( | | μ i s - μ t j | | ϵ c ) ,
Wherein The geometric center and the color average in i dominant color zone of expression source images,
Figure BDA0000115672340000064
Figure BDA0000115672340000065
The geometric center and the color average in j dominant color zone of expression target image, || .|| is for asking for Euclidean distance, ε gAnd ε cBe respectively the contribution factor of control geological information and colouring information, be used for controlling geometric position information and colouring information shining upon the contribution of cost.In the method for the present invention, ε gUsually at [1.5,2.0] interval value, ε cUsually in [1.0,1.5] interval value.
After trying to achieve above-mentioned optimum mapping probability
Figure BDA0000115672340000066
, utilize this optimum mapping to recomputate the color average and the color variance in dominant color zone in the source images.Each source images dominant color zone definition is shone upon as follows:
Φ ( μ i s ) = Σ j = 1 M t w ij * μ t j Σ j = 1 M t w ij * , Φ ( σ i s ) = Σ j = 1 M t w ij * σ t j Σ j = 1 M t w ij * ,
is the color variance in i dominant color zone of source images; is the color variance in j dominant color zone of target image, i new color average and color variance in dominant color zone of
Figure BDA00001156723400000612
Figure BDA00001156723400000613
expression source images.
Utilize new color average and the color variance in source images dominant color zone; Can further upgrade the color of all pixels in this zone, all pixels in i the dominant color zone of source images are shone upon::
I o ( x , y ) = Φ ( σ i s ) σ i s ( I s ( x , y ) - μ i s ) + Φ ( μ i s ) ,
Figure BDA00001156723400000616
I o(x is y) for being positioned at source images (x, the color after the pixel of y) the locating mapping.Yet, directly utilize the characteristic in single dominant color zone that the color of pixel is upgraded after, can produce tangible partitioning boundary between the dominant color zone.Tangible partitioning boundary occurs when preventing to upgrade the pixel color of source images, method of the present invention is at the color I that upgrades a certain pixel of source images s(x in the time of y), not only utilizes the dominant color zone at this pixel place
Figure BDA0000115672340000071
Mapping, also combine simultaneously
Figure BDA0000115672340000072
The mapping in all of its neighbor dominant color zone.These are used to upgrade pixel color I s(x, zone y) are defined as pixel I s(x, y) in abutting connection with dominant color regional ensemble NB (x, y):
Figure BDA0000115672340000073
so: or
Figure BDA0000115672340000075
or
Figure BDA0000115672340000076
or
Figure BDA0000115672340000077
For in abutting connection with dominant color regional ensemble NB (x, each dominant color zone in y)
Figure BDA0000115672340000078
Figure BDA0000115672340000079
Pixel I s(x is y) with probability jP XyBelong to this dominant color zone
Figure BDA00001156723400000710
Method of the present invention is by following formal definition probability jP Xy:
P xy j = 1 Z D ( I s ( x , y ) , R j s ) ,
Z = Σ R j s ∈ NB ( x , y ) D ( I s ( x , y ) , R j s ) ,
Z is a normalized factor,
Figure BDA00001156723400000713
Be pixel I s(x is y) with the dominant color zone
Figure BDA00001156723400000714
Similarity.In order to let the space distribution of target image dominant color pass to source images, method of the present invention combines geometric similarity and color similarity to calculate similarity measurement
Figure BDA00001156723400000715
D ( I s ( x , y ) , R j s ) = e ( - Sqrt ( ( x - x j ) 2 + ( y - y j ) 2 ) δ g ) * e ( - | | I s ( x , y ) - μ s j | | δ c ) , c j s = x j y j Be j dominant color zone of source images
Figure BDA00001156723400000718
Geometric center, δ gAnd δ cBe contribution factor, be used to control the Relative Contribution of geometric similarity and color similarity.δ gUsually at [0.8,1.0] interval value, δ cUsually in [1.0,1.2] interval value.
According to above-mentioned mapping of trying to achieve and probability, to each color of pixel I in the source images s(x y) upgrades, and obtains exporting color I o(x, y).The renewal process of color is following:
I o ( x , y ) = Σ R s j ∈ NB ( x , y ) ( P xy j * ( Φ ( σ i s ) σ i s ( I ( x , y ) - μ i s ) + Φ ( μ i s ) ) ) ,
jP XyBe pixel I s(x y) belongs to j dominant color zone of source images
Figure BDA00001156723400000720
Probability,
Figure BDA00001156723400000721
With
Figure BDA00001156723400000722
Be j new color average and color variance in dominant color zone of source images.
The color transmittance process that method of the present invention relates to, except that image segmentation was used the LUV color space, all the other steps were all used CIEL *a *b *Color space.When the output color of calculating pixel, can only change the tone value a of its color *And b *, the original brightness value L of retaining color *Thereby, make the gradient of source images and detail content preserve.
Realize method described in the invention with C Plus Plus and opencv2.1, and used the sample image of various different colours styles to test.All experiments all are at one
Figure BDA0000115672340000081
Core TM2Quad CPU Q6600 accomplishes on the PC of 2.40GHz 2G internal memory.
Accompanying drawing 4 has provided the source images dominant color quantity synthetic result under the target image situation.(a) among Fig. 4 is source images, and (b) among Fig. 4 is target image, the result that (c) among Fig. 4 obtains for the inventive method, the result that (d) among Fig. 4 obtains for the method for Dong.This instance utilizes target image (b) to change the color of purple flowering shrubs in the source images.As can be seen from the figure, method of the present invention can all be delivered to all dominant color of target image in the source images, and dominant color has also kept its original space distribution simultaneously.
Accompanying drawing 5 has provided color transmission that source images obtains when close with target image semantic content figure as a result.(a) among Fig. 5 and (b) among Fig. 5 are respectively source images and target image, the result that (c) among Fig. 5 obtains for method of the present invention, the result that (d) among Fig. 5 obtains for the method for Dong.From can finding out the figure as a result, all dominant color that the image that method of the present invention produces has not only been preserved target image have also kept the fade effect of color of object simultaneously, and the part semantic information of target image also has been passed in the source images.
The characteristic and the innovation of method of the present invention are that the space distribution that it is corresponding also is delivered in the source images when transmitting the target image color, thereby all dominant color that guarantee target image can be kept in the result images.And, to the constraint strength of locus, can access the result images of different-effect when changing mapping through control geological information contribution factor.
Above-mentioned experimental result and the method for utilizing color space to distribute color is transmitted can be used for each application of computer graphics and Digital Image Processing, have higher actual application value.
The above; Be merely the embodiment among the present invention, but protection scope of the present invention is not limited thereto, anyly is familiar with this technological people in the technical scope that the present invention disclosed; Conversion or the replacement expected can be understood, all of the present invention comprising within the scope should be encompassed in.

Claims (9)

1. method of utilizing color space to distribute color is transmitted is characterized in that comprising that step is following:
Step S1: combining image color of pixel and spatial positional information, utilize the average drifting algorithm that source images and target image are divided into a plurality of dominant color zone;
Step S2: regard the pixel in the dominant color zone as a sample set; Calculate in each dominant color zone in source images, the target image, obtains the following characteristic in each dominant color zone: all pixels that color average, color variance, geometric center and this dominant color zone comprise;
Step S3: color average and the geometric center information of utilizing the dominant color zone; Between source images dominant color zone and target image dominant color zone, set up the probability mapping of a multi-to-multi; Each dominant color zone of source images is regional with each dominant color that different probability is mapped to target image, ask for source images dominant color zone and make the mapping cost summation between the dominant color zone minimum to probability mapping optimum between the target image dominant color zone;
Step S4: according to the probability mapping of optimum, again source images dominant color zone is calculated, obtain new color average and the color variance in source images dominant color zone;
Step S5: utilize new color average and color variance, each color of pixel in the source images dominant color zone is upgraded.
2. by the described method of utilizing color space to distribute color is transmitted of claim 1, it is characterized in that: said dominant color zone is the contiguous pixels zone that has similar color attribute in source images or the target image.
3. it is characterized in that by the described method of utilizing the color space distribution that color is transmitted of claim 1: the characteristic in calculation sources image dominant color zone comprises as follows:
Figure FDA0000115672330000011
Figure FDA0000115672330000012
Figure FDA0000115672330000021
Figure FDA0000115672330000022
i=1,2,....,M s
In the formula, subscript s representes source images;
Figure FDA0000115672330000023
The set that all pixels are formed in i dominant color zone of expression source images,
Figure FDA0000115672330000024
The geometric center in i dominant color zone of expression source images,
Figure FDA0000115672330000025
The sum of all pixels in i dominant color zone of expression source images, I s(x, y) expression be positioned in the source images position (x, the pixel color of y) locating,
Figure FDA0000115672330000026
The color average in i dominant color zone of expression source images,
Figure FDA0000115672330000027
The color variance in i dominant color zone of expression source images,
Figure FDA0000115672330000028
I dominant color zone of expression source images, M sThe dominant color region quantity of expression source images; || .|| is for asking for Euclidean distance.
4. it is characterized in that by the described method of utilizing the color space distribution that color is transmitted of claim 1: the characteristic of calculating target image dominant color zone comprises as follows:
Figure FDA0000115672330000029
Figure FDA00001156723300000211
Figure FDA00001156723300000212
j=1,2,....,M t
Subscript t representes target image in the formula;
Figure FDA00001156723300000213
The set that all pixels are formed in j dominant color zone of expression target image, The geometric center in j dominant color zone of expression target image,
Figure FDA00001156723300000215
The sum of all pixels in j dominant color zone of expression target image, I t(x, y) expression be positioned in the target image position (x, the pixel color of y) locating,
Figure FDA00001156723300000216
The color average in j dominant color zone of expression target image,
Figure FDA00001156723300000217
The color variance in j dominant color zone of expression target image,
Figure FDA00001156723300000218
J dominant color zone of expression target image; M tThe dominant color region quantity of expression target image; || .|| is for asking for Euclidean distance.
5. by claim 3 or the 4 described methods of utilizing color space to distribute color is transmitted, it is characterized in that: ask for source images dominant color zone and the minimum step of mapping cost summation between the dominant color zone is comprised to probability mapping optimum between the target image dominant color zone:
If it is w that i dominant color zone of source images is mapped to the probability in j dominant color zone of target image Ij, the mapping cost between the dominant color zone is l Ij, then corresponding cost summation cost is:
cos t = Σ i M s Σ j M t w ij l ij ,
Utilize earthquake distance algorithm compute optimal probability
Figure FDA0000115672330000032
to make mapping cost summation cost minimum be:
Σ i M s Σ j M t w ij * l ij = min w ij Σ i M s Σ j M t w ij l ij ,
Ask for optimum probability
Figure FDA0000115672330000034
The time need satisfy constraint
Figure FDA0000115672330000035
And
Figure FDA0000115672330000036
M in the formula sBe the dominant color region quantity of source images, M tDominant color region quantity for target image.
6. by the described method of utilizing color space to distribute color is transmitted of claim 5, it is characterized in that: calculate the mapping cost function l between the dominant color zone IjComprise:
l ij = e ( | | c i s - c t j | | ϵ g ) * e ( | | μ i s - μ t j | | ϵ c ) ,
Wherein
Figure FDA0000115672330000038
The geometric center and the color average in i dominant color zone of expression source images,
Figure FDA0000115672330000039
Figure FDA00001156723300000310
The geometric center and the color average in j dominant color zone of expression target image, || .|| is for asking for Euclidean distance, ε g, ε cBe respectively the contribution factor of control geological information and colouring information.
7. by the described method of utilizing color space to distribute color is transmitted of claim 5, it is characterized in that: new color average and the color variance in source images dominant color zone represented as follows:
Φ ( μ i s ) = Σ j = 1 M t w ij * μ t j Σ j = 1 M t w ij * , Φ ( σ i s ) = Σ j = 1 M t w ij * σ t j Σ j = 1 M t w ij * ,
Where
Figure FDA00001156723300000313
Figure FDA00001156723300000314
represents the i-th leading source image color regions new colors mean and variance.
8. by the described method of utilizing color space to distribute color is transmitted of claim 3, it is characterized in that: all pixels in i the dominant color zone
Figure FDA00001156723300000315
of source images are shone upon:
I o ( x , y ) = Φ ( σ i s ) σ i s ( I s ( x , y ) - μ i s ) + Φ ( μ i s ) ,
Figure FDA00001156723300000317
I o(x is y) for being positioned at source images (x, the color after the pixel of y) the locating mapping.
9. by the described method of utilizing color space to distribute color is transmitted of claim 3; It is characterized in that: for the pixel in i dominant color zone of source images; Except the mapping of participating in i dominant color zone; Also participate in its mapping process, all mapping result are done weighted mean, obtain the final color of said pixel in abutting connection with the dominant color zone.
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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102903128A (en) * 2012-09-07 2013-01-30 北京航空航天大学 Video image content editing and spreading method based on local feature structure keeping
CN103413331A (en) * 2013-07-29 2013-11-27 北京航空航天大学 High-resolution video image content sparse expression method supporting edition and propagation
CN104700353A (en) * 2015-02-11 2015-06-10 小米科技有限责任公司 Image filter generating method and device
CN105069465A (en) * 2015-07-21 2015-11-18 华南农业大学 Color conversion method based on L0 gradient maintenance
CN107563976A (en) * 2017-08-24 2018-01-09 广东欧珀移动通信有限公司 U.S. face parameter acquiring method, device, readable storage medium storing program for executing and computer equipment
CN109716208A (en) * 2016-09-13 2019-05-03 3M创新有限公司 Single grouping reflective polarizer with the thickness distribution for the low color customization at rake angle
US10552977B1 (en) 2017-04-18 2020-02-04 Twitter, Inc. Fast face-morphing using neural networks
US10699456B2 (en) 2016-05-11 2020-06-30 Magic Pony Technology Limited Developing visual data using a hierarchical algorithm
CN113021898A (en) * 2021-02-24 2021-06-25 深圳市裕同包装科技股份有限公司 Optimization method, optimization device and optimization system for color three-dimensional printing

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101441763A (en) * 2008-11-11 2009-05-27 浙江大学 Multiple-colour tone image unity regulating method based on color transfer
US20090169129A1 (en) * 2007-12-31 2009-07-02 Yun-Chin Li Method for automatically transforming color space and prospect of an imaging device
CN101706965A (en) * 2009-11-03 2010-05-12 上海大学 Method for colorizing regional image on basis of Gaussian mixture model

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090169129A1 (en) * 2007-12-31 2009-07-02 Yun-Chin Li Method for automatically transforming color space and prospect of an imaging device
CN101441763A (en) * 2008-11-11 2009-05-27 浙江大学 Multiple-colour tone image unity regulating method based on color transfer
CN101706965A (en) * 2009-11-03 2010-05-12 上海大学 Method for colorizing regional image on basis of Gaussian mixture model

Cited By (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102903128A (en) * 2012-09-07 2013-01-30 北京航空航天大学 Video image content editing and spreading method based on local feature structure keeping
CN102903128B (en) * 2012-09-07 2016-12-21 北京航空航天大学 The video image content editor's transmission method kept based on Similarity of Local Characteristic Structure
CN103413331A (en) * 2013-07-29 2013-11-27 北京航空航天大学 High-resolution video image content sparse expression method supporting edition and propagation
US9959484B2 (en) 2015-02-11 2018-05-01 Xiaomi Inc. Method and apparatus for generating image filter
CN104700353A (en) * 2015-02-11 2015-06-10 小米科技有限责任公司 Image filter generating method and device
CN104700353B (en) * 2015-02-11 2017-12-05 小米科技有限责任公司 Image filters generation method and device
CN105069465B (en) * 2015-07-21 2018-06-08 华南农业大学 A kind of color changeover method kept based on L0 gradients
CN105069465A (en) * 2015-07-21 2015-11-18 华南农业大学 Color conversion method based on L0 gradient maintenance
US10699456B2 (en) 2016-05-11 2020-06-30 Magic Pony Technology Limited Developing visual data using a hierarchical algorithm
US11386599B2 (en) 2016-05-11 2022-07-12 Twitter, Inc. Feature transfer
CN109716208A (en) * 2016-09-13 2019-05-03 3M创新有限公司 Single grouping reflective polarizer with the thickness distribution for the low color customization at rake angle
CN109716208B (en) * 2016-09-13 2022-04-12 3M创新有限公司 Single-packet reflective polarizer with thickness profile tailored for low color at oblique angles
US10552977B1 (en) 2017-04-18 2020-02-04 Twitter, Inc. Fast face-morphing using neural networks
CN107563976A (en) * 2017-08-24 2018-01-09 广东欧珀移动通信有限公司 U.S. face parameter acquiring method, device, readable storage medium storing program for executing and computer equipment
CN107563976B (en) * 2017-08-24 2020-03-27 Oppo广东移动通信有限公司 Beauty parameter obtaining method and device, readable storage medium and computer equipment
CN113021898A (en) * 2021-02-24 2021-06-25 深圳市裕同包装科技股份有限公司 Optimization method, optimization device and optimization system for color three-dimensional printing

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