Anisotropic Probabilistic Neural Network for Image Interpolation

Authors:
Ching-Han Chen;Chia-Ming Kuo;Tun-Kai Yao;Sheng-Hsien Hsieh
Affiliations:
Department of Computer Science and Information Engineering, National Central University, Taoyuan County, Taiwan 320;Department of Computer Science and Information Engineering, National Central University, Taoyuan County, Taiwan 320;Department of Computer Science and Information Engineering, National Central University, Taoyuan County, Taiwan 320;Department of Electrical Engineering, I-Shou University, Kaohsiung City, Taiwan 840
Venue:
Journal of Mathematical Imaging and Vision
Year:
2014

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Abstract

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.