Author(s)

Jinfeng Xia, Zhizheng Yang, Fang Li, Yuanda Xu, Nan Ma, Chunxing Wang^*

Shandong Normal University, Jinan, China.

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.

Face Hallucination Super Resolution, Convolutional Network Hybrid Algorithm

Xia, J. , Yang, Z. , Li, F. , Xu, Y. , Ma, N. and Wang, C. (2018) Human Face Super-Resolution Based on Hybrid Algorithm. Advances in Molecular Imaging, 8, 39-47. doi: 10.4236/ami.2018.84004.