Digital halftoning using a modified pulse-coupled neural network
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 moire 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.
Digital image processing weight matrice internal activity error diffusion linking coefficient
DUAN Huawei CHEN Guangxue
South China University of Technology, State Key Laboratory of Pulp and Paper Engineering,Guangzhou, 510641: China
国际会议
Third International Conference on Digital Image Processing(ICDIP 2011)(第三届数字图像处理国际会议)
成都
英文
142-147
2011-04-15（万方平台首次上网日期，不代表论文的发表时间）