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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]

Automatic classification of aerial images is one of the most challengeable tasks due to its high-dimension data and complex context. In order to tackle the problems of high feature dimension and linearly inseparable in original data, this paper proposed a recognition algorithm combining kernel dictionary learning and discriminant analysis based on dictionary learning and sparse representation. First of all, it learned a kernel dictionary that explored the underlying structure of data, then obtained the sparse representations of samples by the kernel dictionary. Secondly, it employed the linear discriminant analysis to make these sparse representations more separable. Finally, it used classical support vector machine for classification. Experimental results show that this method based on kernel dictionary learning and discriminant analysis has superior recognition performance in comparison with the methods based on traditional feature extraction in subspace and dictionary learning. [ABSTRACT FROM AUTHOR]