Image identification based on the compound model of wavelet transform and artificial neural networks

The image definition identification method based on the composite model of wavelet transform and neural networks is stronger in image edge character extraction, nonlinear process, self-adapted study and pattern recognition. The paper puts forward an evaluation method of image definition based on the focusing mechanism of simulating person´s eyes by neural networks and on the composite model of wavelet transformation and neural networks. The wavelet component statistics obtained by the wavelet transform are taken as the inputs of the 5 layer BP neural network model. The model identifies the image definition applying the steepest descent method of the additional momentum in a variable step size to adjust the network weights. The compound model is first trained by 75 images from the training set, and then is tested by 102 images from the testing set. The results show that this is a very effective identification method which can obtain a higher recognition rate.