Colorization is the process of coloring monochrome images. It has been widely used in photo processing and scientific illustration. However, colorizing grayscale images is an intrinsic ill-posed and ambiguous problem, with multiple plausible solutions. To address this issue, we develop a novel progressive automatic colorization via iterative generative models (iGM) that can produce satisfactory colorization in an unsupervised manner. In particular, the generative model is exploited in multi-color spaces (e.g., RGB, YCbCr) jointly and enforced with linearly autocorrelative constraint. This is regarded as the key prior information to pave the way for producing the most probable colorization in high-dimensional space. Experiments on indoor and outdoor scenes reveal that iGM produces more realistic and finer results, compared to state-of-the-arts.

中文翻译：

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通过迭代生成模型进行渐进着色

着色是对单色图像进行着色的过程。它已广泛用于照片处理和科学插图。然而，对灰度图像着色是一个固有的不适定和模棱两可的问题，有多种合理的解决方案。为了解决这个问题，我们通过迭代生成模型 (iGM) 开发了一种新颖的渐进式自动着色，它可以以无监督的方式产生令人满意的着色。特别地，生成模型在多色空间（例如，RGB、YCbCr）中被联合利用并通过线性自相关约束来实施。这被认为是为在高维空间中产生最可能的着色铺平道路的关键先验信息。室内和室外场景的实验表明，与最先进的技术相比，iGM 可产生更逼真、更精细的结果。