Abstract
This paper proposes a structure-aware nonlocal energy optimization framework for interactive image colorization with sparse scribbles. Our colorization technique propagates colors to both local intensity-continuous regions and remote texture-similar regions without explicit image segmentation. We implement the nonlocal principle by computing k nearest neighbors in the high-dimensional feature space. The feature space contains not only image coordinates and intensities but also statistical texture features obtained with the direction-aligned Gabor wavelet filter. Structure maps are utilized to scale texture features to avoid artifacts along high-contrast boundaries. We show various experimental results and comparisons on image colorization, selective recoloring and decoloring, and progressive color editing to demonstrate the effectiveness of the proposed approach.
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This work was supported by the National Natural Science Foundation of China under Grant Nos. 61100146 and 61472351, and the Zhejiang Provincial Natural Science Foundation of China under Grant Nos. LY15F020019 and LQ14F020006. Pan was supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant No. 2013BAH24F01.
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Zhao, HL., Nie, GZ., Li, XJ. et al. Structure-Aware Nonlocal Optimization Framework for Image Colorization. J. Comput. Sci. Technol. 30, 478–488 (2015). https://doi.org/10.1007/s11390-015-1538-x
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DOI: https://doi.org/10.1007/s11390-015-1538-x