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Progressive Colorization via Iterative Generative Models
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DOI：10.1109/LSP.2020.3037690.png)
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Abstract
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Colorization is the process of coloring monochrome images. It has been widely used in photo processing and scientific illustration. However, colorizing grayscale images is an intrinsic ill-posed and ambiguous problem, with multiple plausible solutions. To address this issue, we develop a novel progressive automatic colorization via iterative generative models (iGM) that can produce satisfactory colorization in an unsupervised manner. In particular, the generative model is exploited in multi-color spaces (e.g., RGB, YCbCr) jointly and enforced with linearly autocorrelative constraint. This is regarded as the key prior information to pave the way for producing the most probable colorization in high-dimensional space. Experiments on indoor and outdoor scenes reveal that iGM produces more realistic and finer results, compared to state-of-the-arts.
Keywords:
Colorization
generative model
unsupervised learning
multi-color spaces
iterative algorithm
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