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：半色调技术旨在用仅具有两个离散强度级别的像素再现连续色调图像。该技术已广泛应用于各类打印机，其中大部分采用快速方法（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，通过搜索最优半色调解来追求视觉愉悦的传统方法则受限于其高计算成本。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，我们将半色调问题建模为强化学习问题，其中每个二值像素的值被视为共享全卷积神经网络策略的虚拟智能体所选择的动作。在离线阶段，利用有效梯度估计器训练智能体在单步动作中生成高质量半色调。随后，通过一次快速CNN推理即可在线生成半色调。此外，我们提出了一种新型各向异性抑制损失函数，用以获得理想的蓝噪声特性。最后，我们发现直接优化SSIM会导致平坦区域出现孔洞，而通过将度量与连续色调图像的对比度图加权可避免此现象。实验表明，我们的框架能有效训练轻量级CNN（较现有结构感知方法快15倍），生成视觉质量令人满意的蓝噪声半色调。同时，我们给出了深度多色调原型以证明本方法的可扩展性。