A novel image interpolator based on probabilistic neural network with shapeness/smoothness adaptation

Chinghan Chen, Shenghsien Hsieh

研究成果: 雜誌貢獻會議論文同行評審

摘要

In this paper, we propose a novel image interpolator based on Probabilistic Neural Network(PNN) that adjusts automatically the smoothing parameters of interpolative model for varied smooth/edge image region. This method takes into consideration both smoothness(flat region) and sharpness(edge region) characteristics at the same model. A single neuron, combined with PSO training, is used for sharpeness/smoothness adaptation. The experimental results demonstrate that this interpolator possesses better performance than bicubic polynomial interpolation either at flat region or at edge region of images.

原文???core.languages.en_GB???
頁(從 - 到)698-706
頁數9
期刊Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3497
發行號II
DOIs
出版狀態已出版 - 2005
事件Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China
持續時間: 30 5月 20051 6月 2005

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