Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Thevenaz, P., Blu, T., Unser, M.: Interpolation Revisited. IEEE Trans. Medical Imaging 19, 739–758 (2000)
Key, R.S.: Cubic Convolution Interpolation for Digital Image Processing. IEEE Trans. Acoustics, Speech, Signal Processing 29, 1153–1160 (1981)
Thurnhofer, S., Mitra, S.: Edge-Enhanced Image Zooming. Optical Engineering 35, 1862–1870 (1996)
Battiato, S., Gallo, G., Stanco, F.: A locally Adaptive Zooming Algorithm for Digital Images. Image and Vision Computing 20, 805–812 (2002)
Dai, D., Shih, T., Chau, F.: Polynomial Preserving Algorithm for Digital Image Interpolation. Signal Processing 67, 109–121 (1998)
Arandiga, F., Donat, R., Mulet, P.: Adaptive Interpolation of Images. Signal Processing 83, 459–464 (2003)
Specht, D.F.: Probabilistic Neural Networks for Classification, Mapping, or Associative Memory. IEEE International Conference on Neural Networks 1, 525–532 (1988)
Specht, D.F.: Enhancements to Probabilistic Neural Networks. In: International Joint Conference on Neural Networks, vol. 1, pp. 761–7687 (1992)
Chen, C.: Automatic Design of Neural Networks Based on Genetic Algorithms. In: ICS 1998 Workshop on Artificial Intelligence, pp. 8–13 (1998)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. IEEE Int. Conf. Neural Networks, Perth, Australia, pp. 1942–1948 (1995)
Kennedy, J.: The Particle Swarm: Social Adaptation of Knowledge. In: Proc. 1997 Int. Conf. Evolutionary Computation, Indianapolis, pp. 303–308 (1997)
Al-Fahoum, A.S., Reza, A.M.: Combined Edge Crispiness and Statistical Differencing for Deblocking JPEG Compressed Images. IEEE Trans. Image Processing 10, 1288–1298 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, C., Hsieh, S. (2005). A Novel Image Interpolator Based on Probabilistic Neural Network with Shapeness/Smoothness Adaptation. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_113
Download citation
DOI: https://doi.org/10.1007/11427445_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
eBook Packages: Computer ScienceComputer Science (R0)