Abstract
A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth, edge or detail texture region according to variance-sum criteria function of the feature vectors. Then parameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration, and network weight value matrix is updated by the output of GMM. Since GMM is used, the regularization parameters share properties of different kind of regions. In addition, the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system, and it has strong generalization capability. Comparing with non-adaptive and some adaptive image restoration algorithms, experimental results show that the proposed algorithm obtains more preferable restored images.
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References
M. R. Banham, A. K. Katsaggelos. Digital image restoration. IEEE Signal Processing Magazine, 14 (1997)2, 24–41.
A. K. Katsaggelos, M. G. Kang. Spatially adaptive iterative algorithm for the restoration of astronomical images. International Journal of Image System Technology, 6(1995)4, 1995, 305–313.
S. Perry, L. Guan. Weight assignment for adaptive image restoration by neural networks. IEEE Trans. on Neural Network, 11(2000)1, 156–170.
H. S. Wong, L. Guan. A neural network approach for adaptive image restoration using a fuzzy model-based network architecture. IEEE Trans. on Neural Network, 12(2001)3, 516–531.
Y. T. Zhou, et al. Image restoration using a neural network. IEEE Trans. on Acoustics, Speech & Signal Processing, 36(1998)7, 1141–1151.
M. G. Kang, A. K. Katsaggelos. General choice of regularization functional in regularized image restoration. IEEE Trans. on Image Processing, 4(1995)5, 594–602.
Z. Q. Bian, X. G. Zhang, et al. Pattern Recognition. Beijing, Tsinghua University Press, 2000, 280–282, (in Chinese). 边肇祺, 张学工等. 模式识别, (第二版). 北京, 清华大学出版社, 2000, 280–282.
S. Perry, H. S. Wong, L. Guan. Adaptive Image Processing: A Computational Intelligence Perspective. New York, CRC Press, 2002, 87–96.
S. Palmer, M. Razaz, D. P. Mandic. Spatially adaptive image restoration by neural network filtering. IEEE Proceeding on VII Brazilian Symposium on Neural Network 2002, Pernambuco, Brazil, November 11–14, 2002, 184–189.
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Liu, P., Zhang, Y. & Mao, Z. A Gaussian Mixture Model-based regularization method in adaptive image restoration. J. of Electron.(China) 24, 83–89 (2007). https://doi.org/10.1007/s11767-005-0125-7
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DOI: https://doi.org/10.1007/s11767-005-0125-7
Key words
- Image processing
- Gaussian Mixture Model (GMM)
- Hopfield Neural Network (Hopfield-NN)
- Regularization
- Adaptive image restoration