TITLE:
Human Face Super-Resolution Based on Hybrid Algorithm
AUTHORS:
Jinfeng Xia, Zhizheng Yang, Fang Li, Yuanda Xu, Nan Ma, Chunxing Wang
KEYWORDS:
Face Hallucination Super Resolution, Convolutional Network Hybrid Algorithm
JOURNAL NAME:
Advances in Molecular Imaging,
Vol.8 No.4,
September
14,
2018
ABSTRACT: 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.