一种基于CMAC的图象恢复算法

由于影响成象和导致图象退化的因素具有模糊性和不确定性，很难准确地建立图象退化过程的数学模型，因而建立退化过程的逆过程图象恢复十分困难，为了解决这一问题，提出了一种基于CMC的图象恢复算法，该方法利用CMAC神经网络的非线性映射和综合能力，通过对影响成象和导致图象退化的过程进行反向学习来恢复图象。仿真结果表明，用CMAC神经网络能很好地恢复出已退化的图象，并且神经网络模型与学习方法十分简单，便于实时图象恢复。

The Image Restoration Based on CMAC

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.