HISTOGRAM-GUIDED SEMANTIC-AWARE COLORIZATION

User-guided colorization can predict the colors of a grayscale image according to user inputs, including exemplar images, local inputs and global inputs. Global inputs-based methods are probably the easiest ones to use, but are hard to distribute the input colors into correct regions, due to the lack of color-semantic correspondences. In this paper, we propose a novel histogram-guided semantic-aware colorization method, which explicitly builds the correspondences between global colors and local features with an attention mechanism and uses a differentiable histogram loss to impose the histogram of the results. Our method starts with a semantic-aware subnetwork to build the color-semantic correspondences, followed by a colorization subnetwork to reconstruct the color channels. Experiments demonstrate that our method can effectively control the results with the input histogram. Extensive visual, numerical and user study comparisons show that our method outperforms other global input-based state-of-the-art methods in color naturalness and consistency.