  Deep networks have shown impressive performance in the image restoration
tasks, such as image colorization. However, we find that previous approaches
rely on the digital representation from single color model with a specific
mapping function, a.k.a., color space, during the colorization pipeline. In
this paper, we first investigate the modeling of different color spaces, and
find each of them exhibiting distinctive characteristics with unique
distribution of colors. The complementarity among multiple color spaces leads
to benefits for the image colorization task.
  We present MultiColor, a new learning-based approach to automatically
colorize grayscale images that combines clues from multiple color spaces.
Specifically, we employ a set of dedicated colorization modules for individual
color space. Within each module, a transformer decoder is first employed to
refine color query embeddings and then a color mapper produces color channel
prediction using the embeddings and semantic features. With these predicted
color channels representing various color spaces, a complementary network is
designed to exploit the complementarity and generate pleasing and reasonable
colorized images. We conduct extensive experiments on real-world datasets, and
the results demonstrate superior performance over the state-of-the-arts.
