This paper first analyzes the one-dimensional Gabor function and expands it to a two-dimensional one. The two-dimensional Gabor function generates the two-dimensional Gabor wavelet through measure stretching and rotation. At last, the two-dimensional Gabor wavelet transform is employed
to extract the image feature information. Based on the BP neural network model, the image intelligent test model based on the Gabor wavelet and the neural network model is built. The human face image detection is adopted as an example. Results suggest that, when the method combining Gabor
wavelet transform and the neural network is used to test the human face, it will not influence the detection results despite of complex textures and illumination variations on face images. Besides, when ORL human face database is used to test the model, the human face detection accuracy can
reach above 0.93.
Image Denoising Based on Dual-tree Complex Wavelet Transform and Convolutional Neural Network