Probabilistic moving least squares with spatial constraints for nonlinear color transfer between images

Youngbae Hwang; Joon Young Lee; In So Kweon; Seon Joo Kim

doi:10.1016/j.cviu.2018.11.001

Probabilistic moving least squares with spatial constraints for nonlinear color transfer between images

Youngbae Hwang, Joon Young Lee, In So Kweon, Seon Joo Kim

College of Computing

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

Abstract

The color of a scene may vary from image to image because the photographs are taken at different times, with different cameras, and under different camera settings. To align the color of a scene between images, we introduce a novel color transfer framework based on a scattered point interpolation scheme. Compared to the conventional color transformation methods that use a parametric mapping or color distribution matching, we solve for a full nonlinear and nonparametric color mapping in the 3D RGB color space by employing the moving least squares framework. We further strengthen the transfer with a probabilistic modeling of the color transfer in the 3D color space as well as spatial constraints to deal with mis-alignments, noise, and spatially varying illumination. Experiments show the effectiveness of our method over previous color transfer methods both quantitatively and qualitatively. In addition, our framework can be applied for various instances of color transfer such as transferring color between different camera models, camera settings, and illumination conditions, as well as for video color transfers.

Original language	English
Pages (from-to)	1-12
Number of pages	12
Journal	Computer Vision and Image Understanding
Volume	180
DOIs	https://doi.org/10.1016/j.cviu.2018.11.001
Publication status	Published - 2019 Mar

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) ( NRF-2016R1A2B4014610 ), by Ministry of Culture, Sports and Tourism(MCST) and Korea Content Agency(KOCCA) in the Culture Technology(CT) Research & Development Program 2015, and the Cross-Ministry Giga KOREA Project (MSIT) No. GK18P0200 .

Publisher Copyright:
© 2019 Elsevier Inc.

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Computer Vision and Pattern Recognition

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10.1016/j.cviu.2018.11.001

Cite this

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title = "Probabilistic moving least squares with spatial constraints for nonlinear color transfer between images",

abstract = "The color of a scene may vary from image to image because the photographs are taken at different times, with different cameras, and under different camera settings. To align the color of a scene between images, we introduce a novel color transfer framework based on a scattered point interpolation scheme. Compared to the conventional color transformation methods that use a parametric mapping or color distribution matching, we solve for a full nonlinear and nonparametric color mapping in the 3D RGB color space by employing the moving least squares framework. We further strengthen the transfer with a probabilistic modeling of the color transfer in the 3D color space as well as spatial constraints to deal with mis-alignments, noise, and spatially varying illumination. Experiments show the effectiveness of our method over previous color transfer methods both quantitatively and qualitatively. In addition, our framework can be applied for various instances of color transfer such as transferring color between different camera models, camera settings, and illumination conditions, as well as for video color transfers.",

author = "Youngbae Hwang and Lee, {Joon Young} and Kweon, {In So} and Kim, {Seon Joo}",

note = "Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) ( NRF-2016R1A2B4014610 ), by Ministry of Culture, Sports and Tourism(MCST) and Korea Content Agency(KOCCA) in the Culture Technology(CT) Research & Development Program 2015, and the Cross-Ministry Giga KOREA Project (MSIT) No. GK18P0200 . Publisher Copyright: {\textcopyright} 2019 Elsevier Inc.",

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doi = "10.1016/j.cviu.2018.11.001",

language = "English",

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AU - Hwang, Youngbae

AU - Lee, Joon Young

AU - Kweon, In So

AU - Kim, Seon Joo

N1 - Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) ( NRF-2016R1A2B4014610 ), by Ministry of Culture, Sports and Tourism(MCST) and Korea Content Agency(KOCCA) in the Culture Technology(CT) Research & Development Program 2015, and the Cross-Ministry Giga KOREA Project (MSIT) No. GK18P0200 . Publisher Copyright: © 2019 Elsevier Inc.

PY - 2019/3

Y1 - 2019/3

N2 - The color of a scene may vary from image to image because the photographs are taken at different times, with different cameras, and under different camera settings. To align the color of a scene between images, we introduce a novel color transfer framework based on a scattered point interpolation scheme. Compared to the conventional color transformation methods that use a parametric mapping or color distribution matching, we solve for a full nonlinear and nonparametric color mapping in the 3D RGB color space by employing the moving least squares framework. We further strengthen the transfer with a probabilistic modeling of the color transfer in the 3D color space as well as spatial constraints to deal with mis-alignments, noise, and spatially varying illumination. Experiments show the effectiveness of our method over previous color transfer methods both quantitatively and qualitatively. In addition, our framework can be applied for various instances of color transfer such as transferring color between different camera models, camera settings, and illumination conditions, as well as for video color transfers.

AB - The color of a scene may vary from image to image because the photographs are taken at different times, with different cameras, and under different camera settings. To align the color of a scene between images, we introduce a novel color transfer framework based on a scattered point interpolation scheme. Compared to the conventional color transformation methods that use a parametric mapping or color distribution matching, we solve for a full nonlinear and nonparametric color mapping in the 3D RGB color space by employing the moving least squares framework. We further strengthen the transfer with a probabilistic modeling of the color transfer in the 3D color space as well as spatial constraints to deal with mis-alignments, noise, and spatially varying illumination. Experiments show the effectiveness of our method over previous color transfer methods both quantitatively and qualitatively. In addition, our framework can be applied for various instances of color transfer such as transferring color between different camera models, camera settings, and illumination conditions, as well as for video color transfers.

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Probabilistic moving least squares with spatial constraints for nonlinear color transfer between images

Abstract

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