| 英文标题 | Wavelet-based deep learning algorithm for face super-resolution |
| 作者英文名 | Liu Ying, Sun Dinghua, Gong Yanchao |
| 机构英文名 | Center for Image & Information Processing,Xi'an University of Posts & Telecommunications,Xi'an 710121,China |
| 英文摘要 | At present, super-resolution method based on CNN can obtain excellent results in the evaluation metric of peak signal-to-noise ratio and the structural similarity index, but the visual perceptual quality of super- resolution image is poor, and the details of the facial features are lost. In order to solve these problem, this paper designed a new deep neural network to predict the super-resolution wavelet coefficients to get clear super-resolution face images. Firstly, it used the prior knowledge of face images to manually give the facial features more attention. Then it introduced linear low-rank convolution in the network, and finally used the idea of long-distance dependence to supplement the details of the super-resolution images. The experimental results show that the method in this paper has achieved competitive results in the evaluation metric of peak signal-to-noise ratio and structural similarity index, and the visual perceptual quality of its super-resolution face images are excellent. |
| 英文关键词 | deep learning; convolutional neural network(CNN); wavelet; super resolution(SR) |