基于Gabor小波和CNN的图像失真类型判定算法

For image distortion classification, this paper proposes an algorithm based on Gabor wavelet and CNN (Convolutional Neural Network) . It uses the good characteristic of Gabor wavelet to extract rough feature of images firstly, and then uses the improved CNN to extract the key feature from rough feature. The main steps include: Images are preprocessed firstly (including labels setting, samples balance and samples expansion) ; Then it calculates eight directions Gabor wavelet to preprocessed images, and then add eight sub-bands to one sample for training; Finally, it uses a self-designed CNN and Softmax classifier to train the final model, and uses the methods of random gradient descent and error back propagation to optimize the parameters of convolution kernels during training. The final model is used to determine the type of image distortion, the classification accuracy on the LIVE standard image library is 95.62%, it shows that the proposed method has high accuracy and robustness.