Oversampling and Coarse Quantization for Signals

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Progressive Halftoning by
Perona-Malik Error Diffusion and Stochastic Flipping

Dr. Jackie Shen

Department of Mathematics at University of Minnesota

Abstract:   Halftoning has been a significant topic in image processing due to numerous emerging applications, highly diversified approaches, and enormous challenges in 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. It is based upon 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 successful deterministic halftone algorithms, as well as reveal many interesting features like the blue noise behavior.
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