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Corruptive Artifacts Suppression for Example-Based Color Transfer
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DOI：10.1109/TMM.2014.2305914.png)
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Abstract
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Example-based color transfer is a critical operation in image editing but easily suffers from some corruptive artifacts in the mapping process. In this paper, we propose a novel unified color transfer framework with corruptive artifacts suppression, which performs iterative probabilistic color mapping with self-learning filtering scheme and multiscale detail manipulation scheme in minimizing the normalized Kullback-Leibler distance. First, an iterative probabilistic color mapping is applied to construct the mapping relationship between the reference and target images. Then, a self-learning filtering scheme is applied into the transfer process to prevent from artifacts and extract details. The transferred output and the extracted multi-levels details are integrated by the measurement minimization to yield the final result. Our framework achieves a sound grain suppression, color fidelity and detail appearance seamlessly. For demonstration, a series of objective and subjective measurements are used to evaluate the quality in color transfer. Finally, a few extended applications are implemented to show the applicability of this framework.
Keywords:
Color transfer
computational photograph
edge-preserving smoothing
image manipulation
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