High Quality Halftone

High-quality halftoning aims to reproduce continuous-tone images using only two discrete pixel intensity levels, while preserving fine details and achieving a desirable "blue-noise" texture. Recent research focuses on deep learning approaches, particularly convolutional neural networks (CNNs) and reinforcement learning, to generate superior halftones, often incorporating multi-scale processing and novel loss functions to optimize visual quality and computational efficiency. These advancements are improving the fidelity of image reproduction in printing and display technologies, and also contribute to broader image processing research by addressing challenging inverse problems like halftone restoration.