Image Halftoning with Cellular Neural Networks

Kenneth R. Crounse, Leon O. Chua, Tamás Roska, Tamás Roska

Research output: Contribution to journalArticle

57 Citations (Scopus)


Many algorithms have been devised for halftoning digital images. These algorithms all suffer well-studied defects, which are especially apparent in the case where the resulting image is displayed at the marginally oversampled resolution and viewed at the critical pixel merge distance. Recently, it has been shown that a neural network approach may be useful for halftoning. Here, the feasibility of using neural networks in a practical application is considered. The cellular neural network (CNN) architecture is chosen for its proven implementability in VLSI and high speed operation. Since both the CNN and halftoning have a geometrically local character, the CNN provides a natural implementation. The CNN template weights are derived by analogy to the well-known error diffusion algorithm for halftoning. Some limitations of the neural network approach are analyzed providing an advance in designing template weights over previous methods. These limitations are shown to be especially critical in the case of the small interconnection neighborhoods needed for efficient implementation. Our design criteria are validated by direct simulation. The resulting halftones are shown to be more faithful reproductions of the original than those pro duced by the error diffusion algorithm. It is suggested that a CNN with optical inputs could provide a high-speed scanner/halftoner for applications such as FAX.

Original languageEnglish
Pages (from-to)267-283
Number of pages17
JournalIEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing
Issue number4
Publication statusPublished - Apr 1993

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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