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Wavelet-based image interpolation using multilayer perceptrons

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Abstract

Changing the resolution of digital images and video is needed image processing systems. In this paper, we present nonlinear interpolation schemes for still image resolution enhancement. The proposed neural network interpolation method is based on wavelet reconstruction. With the wavelet decomposition, the image signals can be divided into several time–frequency portions. In this work, the wavelet decomposition signal is used to train the neural networks. The pixels in the low-resolution image are used as the input signal of the neural network to estimate all the wavelet sub-images of the corresponding high-resolution image. The image of increased resolution is finally produced by the synthesis procedure of wavelet transform. In the simulation, the proposed method obtains much better performance than other traditional methods. Moreover, the easy implementation and high flexibility of the proposed algorithm also make it applicable to various other related problems.

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Acknowledgements

This work was supported by the National Science Council, Taiwan, Republic of China, under Grant NSC-91–2213-E-029–021.

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  1. Department of Computer Science and Information Engineering, Tunghai University, 407, Taichung, Taiwan

    Yu-Len Huang

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  1. Yu-Len Huang
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Correspondence to Yu-Len Huang.

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Huang, YL. Wavelet-based image interpolation using multilayer perceptrons. Neural Comput & Applic 14, 1–10 (2005). https://doi.org/10.1007/s00521-004-0433-0

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  • DOI: https://doi.org/10.1007/s00521-004-0433-0

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