In *Cellular Neural Networks and Their Applications Proceedings, 1998 Fifth IEEE International Workshop on*, pages 337 -342, 4, 1998.

doi abstract bibtex

doi abstract bibtex

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 times;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 times;3 halftoning template is used; higher quality halftones can be obtained only by using larger templates. A CNNUM algorithm is introduced which uses only a 3 times;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 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 analyzed. The iterative procedure is demonstrated to produce a visually pleasing halftone

@inproceedings{685397, Author = {Crounse, K.R. and Roska, T. and Chua, L.O.}, Booktitle = {Cellular Neural Networks and Their Applications Proceedings, 1998 Fifth IEEE International Workshop on}, Date-Added = {2012-08-20 14:16:54 +0000}, Date-Modified = {2012-08-20 17:22:52 +0000}, Doi = {10.1109/CNNA.1998.685397}, Keywords = {CNN universal machine;CNNUM algorithm;boundary cell values;cellular neural network;discrete-time system;halftoning;human visual system;image block processing;iterative method;linear filter model;spatial correlations;templates;cellular neural nets;discrete time systems;image processing;iterative methods;optical correlation;parallel algorithms;}, Month = {4}, Pages = {337 -342}, Title = {Some methods for practical halftoning on the CNN universal machine}, Year = {1998}, 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 times;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 times;3 halftoning template is used; higher quality halftones can be obtained only by using larger templates. A CNNUM algorithm is introduced which uses only a 3 times;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 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 analyzed. The iterative procedure is demonstrated to produce a visually pleasing halftone}, Bdsk-File-1 = {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}, Bdsk-Url-1 = {http://dx.doi.org/10.1109/CNNA.1998.685397}}

Downloads: 0