We report the application of modified pulse-coupled neural network (PCNN) models as an image processing tool to improve the quality of digital halftoning. Four factors including weight matrice, internal activity computation, type of error diffusion and linking coefficient were researched and optimized in terms of the PSNR metric and visual inspection on halftoning simulations. Experimental results show that the optimized PCNN model is able to yield satisfying halftoning outputs, which has better quality than that obtained by using the traditional order dither algorithm. Moreover, because of the utilization of random function in the modified PCNN model, simulated images generated from that PCNN model eliminate the periodic visual defect that the order dither innately has and therefore can potentially get rid of moiré pattern if used for printing color image. This research, on the one hand, provides a new way to do digital halftoning, on the other hand, expands the application field of the PCNN method.

Date Published: 8 July 2011
PDF: 6 pages
Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 80090S (8 July 2011); doi: 10.1117/12.896559

Published in SPIE Proceedings Vol. 8009:
Third International Conference on Digital Image Processing (ICDIP 2011)
Ting Zhang, Editor(s)