Image Restoration Using Neural Networks
For resolving a restoration problem of degraded and noisy image, we investigate the Hopfield neural network, and we employ the eliminating highest error EHE criterion in intention to improving perfor- mances of network. Moreover, with the purpose to make a better restoration, we take in consideration a human perception in restoration process. To do this, we introduce an adaptive regularization scheme, with contribution of a local statistical analysis, to assigning each pixel one regular- ization parameter regarding to its spatial activity. Due to various values of regularization parameter, this scheme permit us expanding the one network to a network of network NON, which we subsequently elucidate its analogy with the human visual system, a cortex.
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- 1.Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addisson-Wesley (1992)Google Scholar
- 2.Banham, M.R., Katsaggelos, A.K.: Digital Image Restoration. IEEE Signal Processing Magazine. (March 1997) 24–41Google Scholar
- 3.Paik, J.K., Katsaggelos, A.K.: Image Restoration Using Modified Hopfield Neural network. IEEE Trans. on Image Processing, Vol. 1. (January 1992) 49–63Google Scholar
- 4.Sun, Y.: A Generalized Updating Rule For Modified Hopfield Neural Network For Quadratic Optimization. Neurocomputing. 19 (1998) 133–143Google Scholar
- 5.Sun, Y.: Hopfield Neural Network Based Algorithms and Simulation. IEEE Trans. on Signal Processing, Vol. 48. 7 (July 2000) 2119–2131Google Scholar
- 6.Kang, M.G., Katsaggelos, A. K.: General Choice of the Regularization Functional in Regularized Image Restoration. IEEE Trans. on Image Processing, Vol. 4. 5 (May 1995)Google Scholar
- 7.Anderson, J.A., Sutton J.P.: A network of Networks: Computation and Neurobiology. World Congress of Neural Networks, Vol. 1.(1995) 561–568Google Scholar