Image Denoising Algorithm Based On Concolutional Neural Network

In 2006, Hinton proposed a neural network of deep structure. It sets off a boom in artificial neural networks once again. Deep learning is a relatively sophisticated machine learning algorithm. It has been a great success in the image recognition and speech recognition. Deep learning allows the machine to simulate human visual and thinking, and other activities. It solves many complex pattern recognition problems, which makes the relevant technology of artificial intelligence to get a lot. The combination of deep learning and a variety of practical applications is a very important research work.On the basis of studying the problem of image denoising, this thesis presents a deep network based on convolutional neural network. The network is different from the traditional neural network. It consists of four sub networks, each of which has a different number of hidden layers. The input image is filtered to obtain the feature maps through the convolution operation. Then these feature maps are all connected to the output image.In order to explain the deep network is how to denoise. The image denoising problem is converted into a learning problem in this thesis. Then the deep network learns the error of the output image and label image to modify the weight of each layer in the network, so that it can be used for image denoising. In addition, this thesis introduces the setup of the network, the number of layer, the setting of the training iteration number, the size of the training sample, the construction of the convolution layer, and the setting of the parameters of the model.In the training of the deep network, after the current one sub network training is good, the parameter is initialized as a next network. Then a next sub network is trained. Convolution operation can make the image become small. Image noise must be removed, while the size of the image cannot be changed. In each iteration of training, this thesis selects a region of the input image, which randomly selects a point as the center. It gets the image cover the corresponding parts of the output image through the convolution.In this thesis, three experiments are designed to verify the performance of the proposed algorithm. Experiment one designs an image of 512?512. The different blocks of the image are added to Gauss noise. Experiment two and experiment three respectively add Gauss noise to the local and the overall of the lena image and woman image. The results are analyzed from two aspects of subjective and objective, the results are better than the previous methods.