基于Gabor小波和CNN的图像失真类型判定算法

针对图像失真分类问题，提出了一种基于Gabor小波和卷积神经网络（convolutional neural network，CNN）的失真类型判定算法。该算法先利用Gabor小波的良好特性对图像进行特征粗提取，再通过改进的CNN进一步提取关键特征。算法步骤包括：首先对图像进行预处理（包括标签设定、样本均衡和样本扩充）；然后对预处理后的图像进行八方向的Gabor小波变换，并将不同方向的子带叠加构成输入样本；最后通过自行设计的CNN和Softmax分类器对样本进行训练，训练过程中采用随机梯度下降和反向误差传播的方法对卷积核参数进行优化得到最终模型。对训练好的模型进行失真类型判定实验，在LIVE标准图像库上分类正确率达95.62%，表明本算法具有较高的准确性和鲁棒性。

Image distortion judgement based on Gabor wavelet and CNN

For image distortion classification, this paper proposed an algorithm based on Gabor wavelet and CNN. It used the good characteristic of Gabor wavelet to extract rough feature of images firstly, and then used the improved CNN to extract the key feature from rough feature. The main steps included pre-processing image firstly(including labels setting, samples balance and samples expansion); then it calculated eight directions Gabor wavelet to preprocessed images, and aded eight sub-bands to one sample for training; finally, it used a self-designed CNN and Softmax classifier to train the final model, and used the methods of random gradient descent and error back propagation to optimize the parameters of convolution kernels during training. Using the final model 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.