A New Art-lms Neural Network for Adaptive Image Restoration
نویسنده
چکیده
A neural network design – the adaptive resonance theory least mean square (ART-LMS) neural network – is proposed for the restoration of images corrupted by impulse noise. The network design is based on the concept of a counterpropagation network (CPN). The ART network automatically uses a vigilance parameter to generate the cluster layer node for the Kohonen learning algorithm in CPN. In addition, the LMS learning algorithm is used to adjust the weight vectors between the cluster layer and the output layer for the Grossberg learning algorithm in CPN. The LMS algorithm is used to obtain the optimal weight for each cluster independently and minimizes the mean square error of the filter output. Experimental results show that the proposed filter based on proposed ART-LMS outperforms many well-accepted conventional filters in terms of noise suppression and detail preservation.
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