Inverse Halftoning

Inverse halftoning aims to reconstruct a continuous-tone image from its halftoned, binary representation—a challenging inverse problem due to significant information loss during the halftoning process. Current research focuses on deep learning approaches, particularly convolutional neural networks (CNNs) and multi-agent reinforcement learning, to improve the accuracy and efficiency of reconstruction, often incorporating techniques like multiscale processing and novel loss functions to enhance detail preservation and blue-noise characteristics. These advancements hold significant potential for improving image quality in printing and display technologies, as well as contributing to broader image restoration and processing techniques.