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Updated: Jul 12, 2026
04:43
Visualizing Visual Adaptation
Published on: April 24, 2017
Variational models for image colorization via chromaticity and brightness decomposition
1Department of Mathematics, University of Kentucky, Lexington, KY 40515, USA. skang@ms.uky.edu
Summary
This study introduces new variational models for image colorization, enhancing grayscale images using chromaticity. The proposed methods, including total variation (TV) minimizing and weighted harmonic maps, effectively recover color information.
Area of Science:
- Computer Vision
- Image Processing
- Computational Mathematics
Background:
- Image colorization is a challenging task that aims to restore color to grayscale images.
- Existing methods often struggle with accurately recovering color information, especially with limited color input.
Purpose of the Study:
- To develop novel variational models for image colorization.
- To leverage chromaticity color components for improved color recovery.
- To explore extensions for texture colorization.
Main Methods:
- Proposed two variational models: total variation (TV) minimizing colorization and weighted harmonic maps for colorization.
- Utilized chromaticity color components for color restoration.
- Introduced penalized versions of the models and analyzed their convergence properties.
Main Results:
- Demonstrated successful color recovery in grayscale images using the proposed variational models.
- The weighted harmonic maps model effectively incorporated edge information from brightness data for smoother color reconstruction.
- Numerical results showcased the efficacy of the models, including an extension to texture colorization.
Conclusions:
- The developed variational models offer a robust approach to image colorization.
- The integration of chromaticity and edge information leads to enhanced color recovery.
- The models show potential for applications in image processing and computer vision.
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