Halftoning with Multi-Agent Deep Reinforcement Learning
This addresses the need for efficient and high-quality digital halftoning in printing and display applications, representing an incremental improvement over existing deep methods.
The paper tackled the problem of generating high-quality halftone images with blue-noise properties using deep learning, proposing HALFTONERS, a multi-agent deep reinforcement learning method that achieved improved halftone quality while maintaining speed.
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.