Application Research On Image Super Resolution Algorithm Based On Convolutional Neural Network

Recently, image super-resolution reconstruction technology has made rapid development in the life, military, satellite remote sensing and other fields. With the development of deep learning technology, convolutional neural network is applied to image super-resolution processing. As a super-resolution reconstruction algorithm of image based on the learning,SRCNN has better performance in single frame images. Because it has good characteristics such as simple image preprocessing,consistent network structure and relatively lower training parameters. However, it has not been used for sequence image super-resolution processing.In traditional image super-resolution reconstruction algorithm, the motion estimation in essence uses the information from sequence images on time dimension. In this paper, we uses convolution neural network for spatial-temporal information extraction. Based on SRCNN in single frame images, we study 3D convolutional kernel using for sequence image. The main work of this paper is as follows:(1) We study 3D convolutional kernel for spatial-temporal information extraction in sequence images super-resolution processing.Convolutional neural network is used in spatial-temporal information extraction instead of traditional motion estimation to avoid wrong matches.A super-resolution model in sequence images based on 3D convolution kernel is established, and achieves good results.(2) In order to improve the efficiency of the super-resolution algorithm in sequence images based on 3D convolution kernel, we improve the above model with the sparse representation of the dictionary learning algorithm. The method is that to reduce the size of the network,we obtain the over completed dictionary corresponding to the high and low resolution images with convolution neural network, Meanwhile,experimental results show that Super-resolution reconstruction efficiency has further improved.(3) We adopt the GPU algorithm to train and reconstruct the super-resolution. The algorithm of image super-resolution reconstruction based on 3D convolution kernel is completed and verified.