Proceedings Article | 16 February 2006
Proc. SPIE. 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning
KEYWORDS: Diffusion, Halftones, Stochastic processes, Phase modulation, Image processing, Binary data, Visual process modeling, Image enhancement, Anisotropic diffusion, Error analysis
Halftoning has been a significant topic in image processing due to many emerging
applications, various diversified approaches, and challenging theoretical analysis.
Inspired by the wealthy literature on halftoning, as well as the recent PDE (partial
differential equations) approach in image processing, the current work proposes a novel
progressive halftoning algorithm by empolying the celebrated anisotropic diffusion model
of Perona and Malik (IEEE Trans. Pattern Anal. Machine Intell., 12:629-639, 1990),
and a properly designed stochastic strategy for binary flipping. The halftone outputs
from the proposed model are typical samples of some random fields, which share many
virtues of existent deterministic halftone algorithms, as well as show many interesting
features like the blue noise behavior. The new model is independent of traditional
windows, tiles, or paths, and allows direct parallel implementation.
