Gray2ColorNet: Transfer More Colors from Reference Image

Image colorization is an effective approach to provide plausible colors for grayscale images, which can achieve better and pleasing visual qualities. Although exemplar based colorization approaches provide promising results, they are relied on semantic colors or global colors only from the reference images. For the former situation, when the correspondence between the input grayscale image and reference image is not established, the colors of the reference image cannot be transferred to the input grayscale image successfully. With the later circumstance, because only global colors are considered, it is hard to produce a color image whose objects have the same color as the reference image when they are semantically related. Thus, an end-to-end colorization network Gray2ColorNet is proposed in this work, where an attention gating mechanism based color fusion network is designed to accomplish the colorization tasks. Relied on the proposed method, the semantic colors and global color distribution from the reference image are fused effectively, which are transferred to the final color images along with the prior knowledge of colors contained in the training data. The experimental results demonstrate the superior colorization performances of the proposed method compared to other state-of-the-art approaches.