摘要： This study proposes a novel image interpolation method based on an anisotropic probabilistic neural network (APNN). The proposed method uses an anisotropic Gaussian kernel to improve image interpolation, which causes blurred edges. The objective of this anisotropic Gaussian kernel-based probabilistic neural network is to provide high adaptivity of smoothness/sharpness during image/video interpolation. This APNN interpolation method adjusts the smoothing parameters for varied smooth/edge regions, and considers edge direction. This APNN uses a single neuron to estimate sharpness/smoothness. The proposed method achieves better sharpness enhancement at edge regions, and reveals the noise reduction at smooth region. This study also uses interpolating a slanted-edge image to reveal blurring and blocking effects. Finally, this study compares the performance of these proposed methods with other image interpolation methods. 其他題名： J Math Imaging Vis 出版者： Boston: Springer US 出版日期： 2014-03-01 出處： Journal of mathematical imaging and vision, 2014-03, Vol.48 (3), p.488-498 版權： Springer Science+Business Media New York 2013 版權： 2015 INIST-CNRS 版權： Springer Science+Business Media New York 2013. 識別號： ISSN: 0924-9907 識別號： EISSN: 1573-7683 識別號： DOI: 10.1007/s10851-013-0424-9
