基于卷积神经网络的视频图像超分辨率重建方法.

In order to further improve the performance of video image super-resolution reconstruction and study the reconstruction of spatial resolution of video images by using the characteristics of convolution neural network .this paper proposed a video image reconstruction model based on convolution neural network. Hie model adopted the pre-training strategy to initialize the parameters. And it carried out the training processing both on the spatial and temporal dimensions of the multi-frame video images at the same time. It extracted the characteristics of the main motion information, learnt and made full use of the information inter the frames for improved performance. And it used the adaptive motion compensation algorithm to optimize the output of the channel to obtain the reconstructed center frame image with high resolution. The experimental results show that the average of objective evaluation indexes for video image reconstruction improves with a rather clear margin (PSNR + 0. 4 dB/SSIM + 0.02) ,and the edge of the fuzzy phenomenon in video reconstruction image for the subjective visual effect is effectively reduced. Compared with other traditional algorithms,it both obviously improved the evaluation of the objective indexes and subjective visual effect of the reconstructed image. Providing a novel architecture based on convolution neural network for video image super-resolution, which provides an exploration for the further study of video image super-resolution reconstruction based on the deep learning method. [ABSTRACT FROM AUTHOR]