Progressive halftoning by Perona-Malik error diffusion and stochastic flipping

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.