论文标题
关节强度梯度引导的生成建模用于着色
Joint Intensity-Gradient Guided Generative Modeling for Colorization
论文作者
论文摘要
本文提出了一个迭代生成模型，用于解决自动着色问题。尽管以前的研究表明能够产生合理的颜色，但边缘颜色溢出和参考图像的需求仍然存在。本研究中无监督学习的起点是观察到梯度图具有图像的潜在信息。因此，生成建模的推理过程是在关节强度梯度域中进行的。具体而言，作为网络输入，一组强度梯度形成的高维张量用于在训练阶段训练强大的噪声条件得分网络。此外，提出了在迭代着色阶段限制生成模型中生成模型内的自由度的联合强度梯度约束，并且有利于边缘提供边缘。广泛的实验表明，该系统在定量比较或用户研究中都超过了最先进的方法。
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.
