%0 Journal Article
%T Human Face Super-Resolution Based on Hybrid Algorithm
%A Jinfeng Xia
%A Zhizheng Yang
%A Fang Li
%A Yuanda Xu
%A Nan Ma
%A Chunxing Wang
%J Advances in Molecular Imaging
%P 39-47
%@ 2161-6752
%D 2018
%I Scientific Research Publishing
%R 10.4236/ami.2018.84004
%X Aiming at the problems of image super-resolution algorithm with many convolutional neural networks, such as large parameters, large computational complexity and blurred image texture, we propose a new algorithm model. The classical convolutional neural network is improved, the convolution kernel size is adjusted, and the parameters are reduced; the pooling layer is added to reduce the dimension. Reduced computational complexity, increased learning rate, and reduced training time. The iterative back-projection algorithm is combined with the convolutional neural network to create a new algorithm model. The experimental results show that compared with the traditional facial illusion method, the proposed method can obtain better performance.
%K Face Hallucination Super Resolution
%K Convolutional Network Hybrid Algorithm
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=87305