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arXiv · 2207.11408

Halftoning with Multi-Agent Deep Reinforcement Learning

Haitian Jiang·Dongliang Xiong·Xiaowen Jiang·Aiguo Yin·Li Ding·Kai Huang
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Abstract

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.

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Haitian Jiang, Dongliang Xiong, Xiaowen Jiang, Aiguo Yin, Li Ding, Kai Huang. 2022-07-23. Halftoning with Multi-Agent Deep Reinforcement Learning. https://doi.org/10.1109/icip46576.2022.9897198

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