  This paper proposes an iterative generative model for solving the automatic
colorization problem. Although previous researches have shown the capability to
generate plausible color, the edge color overflow and the requirement of the
reference images still exist. The starting point of the unsupervised learning
in this study is the observation that the gradient map possesses latent
information of the image. Therefore, the inference process of the generative
modeling is conducted in joint intensity-gradient domain. Specifically, a set
of intensity-gradient formed high-dimensional tensors, as the network input,
are used to train a powerful noise conditional score network at the training
phase. Furthermore, the joint intensity-gradient constraint in data-fidelity
term is proposed to limit the degree of freedom within generative model at the
iterative colorization stage, and it is conducive to edge-preserving. Extensive
experiments demonstrated that the system outperformed state-of-the-art methods
whether in quantitative comparisons or user study.
