Halftoning aims to reproduce a continuous-tone image with pixels whose intensities are constrained to two discrete levels. This technique has been deployed on every printer, and the majority of them adopt fast methods (e.g., ordered dithering, error diffusion) that fail to render structural details, which determine halftone's quality. Other prior methods of pursuing visual pleasure by searching for the optimal halftone solution, on the contrary, suffer from their high computational cost. In this paper, we propose a fast and structure-aware halftoning method via a data-driven approach. Specifically, we formulate halftoning as a reinforcement learning problem, in which each binary pixel's value is regarded as an action chosen by a virtual agent with a shared fully convolutional neural network (CNN) policy. In the offline phase, an effective gradient estimator is utilized to train the agents in producing high-quality halftones in one action step. Then, halftones can be generated online by one fast CNN inference. Besides, we propose a novel anisotropy suppressing loss function, which brings the desirable blue-noise property. Finally, we find that optimizing SSIM could result in holes in flat areas, which can be avoided by weighting the metric with the contone's contrast map. Experiments show that our framework can effectively train a light-weight CNN, which is 15x faster than previous structure-aware methods, to generate blue-noise halftones with satisfactory visual quality. We also present a prototype of deep multitoning to demonstrate the extensibility of our method.

翻译：半色调化旨在用仅受限于两个离散级别的像素强度再现连续色调图像。该技术已应用于每台打印机，其中多数采用快速方法（如有序抖动、误差扩散），但这些方法无法呈现决定半色调质量的细节结构。相反，以往通过搜索最优半色调方案追求视觉愉悦的方法，则因计算成本高昂而受限。本文提出一种基于数据驱动的快速且结构感知的半色调方法。具体而言，我们将半色调化表述为强化学习问题，其中每个二值像素的值被视为由共享全卷积神经网络策略的虚拟代理选择的动作。在离线阶段，利用高效梯度估计器训练代理在单动作步骤中生成高质量半色调。随后，可通过一次快速CNN在线推理生成半色调。此外，我们提出新型各向异性抑制损失函数，能引入理想的蓝噪声特性。最后，我们发现优化SSIM可能导致平坦区域出现空洞，而通过用连续色调对比度图加权该指标可避免此问题。实验表明，我们的框架能有效训练轻量级CNN（比先前结构感知方法快15倍），生成视觉质量令人满意的蓝噪声半色调。我们还展示了深度多色调化原型，以证明方法的可扩展性。