Deep neural networks have recently succeeded in digital halftoning using vanilla convolutional layers with high parallelism. However, existing deep methods fail to generate halftones with a satisfying blue-noise property and require complex training schemes. In this paper, we propose a halftoning method based on multi-agent deep reinforcement learning, called HALFTONERS, which learns a shared policy to generate high-quality halftone images. Specifically, we view the decision of each binary pixel value as an action of a virtual agent, whose policy is trained by a low-variance policy gradient. Moreover, the blue-noise property is achieved by a novel anisotropy suppressing loss function. Experiments show that our halftoning method produces high-quality halftones while staying relatively fast.
翻译:深神经网络最近成功地利用香草共振层和高度平行的平行层实现了数字半质化。 但是,现有的深层方法未能产生满足的蓝噪音属性的半质,需要复杂的培训计划。 在本文中,我们提出了一个基于多剂深层强化学习的半质法,叫做HALFTONERS,它学会了一种生成高质量半质图像的共同政策。具体地说,我们把每个二进像素值的决定视为一个虚拟代理器的动作,该代理器的政策受到低变异政策梯度的培训。此外,蓝噪音属性是通过新颖的抗生素抑制损失功能实现的。实验显示,我们的半量法在保持相对快速的同时产生高质量的半量子。