The classical image restoration filters are deconvolutioninverse filtering)

Wiener deconvolution

SVD pseudoinverse filter

Kalman filter and maximumentropy restoration etc .Because the universality morbidity of image estoration process

the deconvolution is only suitable for high SNR(Signal to Noise Ratio). Wiener deconvolution needs the knowledge of general stationary process and the correlation function and power spectrum

which makes it difficult to be used in practice. SVD pseudoinverse filter

and Kalman filter is very complex and has large computing work

which restricts its use in practive. The other methods based on if them model

Gauss and Gauss--Markov stochastic process have also been restrictedin real image processing bacause of theri complex. Because factors affecting imaging and making image degeneration are fuzzy and uncertain

it is very difficult to build accurate mathematical model of image degeneration and

which therefore make it impossible to restore the image. Due to its capability of nonlinear mapping and synthesis

CMAC can effectively achieve image restoration by learning reverse process of image degeneration

which resolves the shortcomings of traditionary methods. The simulator results showed that CMAC neural networks are able to restore the degeneration image effectively. The learning algorithm of the network is simply. The present method is convenient for real|time restoring image.