Unsupervised Learning of Local Mean Gray Values for Image Pre-processing
A parallel-sequential unsupervised learning method for image smoothing is presented which can be implemented with a Multi Layer Neural Network. In contrast to older work of the author which has used 4-connectivity of processing elements (neurons) leading to a very big number of recursions now each neuron of network layer t+1 is connected with (2M+1)✻(2M+1) neurons of layer t guaranteeing a significant reduction of network layers with the same good smoothing results.
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