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.
|Number of pages||9|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|State||Published - 2005|
|Event||Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China|
Duration: 30 May 2005 → 1 Jun 2005