30 March 2000 Partitioning schemes for use in a neural network for digital image halftoning
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In this research, we investigate partitioning schemes for reducing the computational complexity of an error diffusion neural network (EDN) for the application of digital halftoning. We show that by partitioning the original image into k subimages, the time required to perform the halftoning using an EDN is reduced by as much as a factor of k. Motivated by this potential speedup, we introduce three approaches to partitioning with varying degrees of overlap and communication between the partitions. We quantitatively demonstrate that the Constrained Framing approach produces halftoned images whose quality is as good as the quality of halftoned images produced by the EDN without partitioning.
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Jean R. S. Blair, Tommy D. Wagner, David A. Nash, Eugene K. Ressler, Barry L. Shoop, Timothy J. Talty, "Partitioning schemes for use in a neural network for digital image halftoning", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380594; https://doi.org/10.1117/12.380594

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