Some methods for practical halftoning on the CNN universal machine

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

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

This paper explores two issues which are relevant in practical halftoning situations on the CNN Universal Machine: block processing of large images with small CNN arrays, and the use of no larger than 3×3 templates. It is shown that block processing can be performed without noticeable boundary artifacts by careful selection of boundary cell values. In this example, a standard 3×3 halftoning template is used, which is known to produce halftones of moderate quality. Higher quality halftones can be obtained only by using larger templates. To overcome this limitation, a CNNUM algorithm is introduced which uses only a 3×3 template but emulates a much larger effective template through an iterative procedure. The method is to discretize the CNN transient in time and then implement the spatial correlations (template operations) at each time step with a CNN transient. An A-B-template pair was designed for a single CNN transient to approximate a very simple linear filter model of the human visual system. The resulting discrete-time system was then analyzed in the linear region to choose the optimal time step. The iterative procedure is demonstrated to produce a visually pleasing halftone.

Original languageEnglish
Pages337-342
Number of pages6
Publication statusPublished - Jan 1 1998
EventProceedings of the 1998 5th IEEE International Workshop on Cellular Neural Networks and Their Applications, CNNA - London, UK
Duration: Apr 14 1998Apr 17 1998

Other

OtherProceedings of the 1998 5th IEEE International Workshop on Cellular Neural Networks and Their Applications, CNNA
CityLondon, UK
Period4/14/984/17/98

ASJC Scopus subject areas

  • Software

Cite this

Crounse, K. R., Roska, T., & Chua, L. O. (1998). Some methods for practical halftoning on the CNN universal machine. 337-342. Paper presented at Proceedings of the 1998 5th IEEE International Workshop on Cellular Neural Networks and Their Applications, CNNA, London, UK, .