Human Face Super-Resolution Based on Hybrid Algorithm ()
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.
Share and Cite:
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.
Cited by
No relevant information.