Deep Exemplar-based Colorization
This is the implementation of paper Deep Exemplar-based Colorization by Mingming He*, Dongdong Chen*, Jing Liao, Pedro V. Sander and Lu Yuan in ACM Transactions on Graphics (SIGGRAPH 2018) (*indicates equal contribution).
Deep Exemplar-based Colorization is the ﬁrst deep learning approach for exemplar-based local colorization. Given a reference color image, our convolutional neural network directly maps a grayscale image to an output colorized image.
The proposed network consists of two sub-networks, Similarity Sub-net which computes the semantic similarities between the reference and the target, and Colorization Sub-net which selects, propagates and predicts the chrominances channels of the target.
The input includes a grayscale target image, a color reference image and bidirectional mapping functions. We use Deep Image Analogy as default to generate bidirectional mapping functions. It is applicable to replace with other dense correspondence estimation algorithms.
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