Projects

Image Super-Resolution via Deep Recursive Residual Network
Ying Tai, Jian Yang, Xiaoming Liu

Recently, Convolutional Neural Network (CNN) based models have achieved great success in Single Image SuperResolution (SISR). Owing to the strength of deep networks, these CNN models learn an effective nonlinear mapping from the low-resolution input image to the high-resolution target image, at the cost of requiring enormous parameters. This paper ...
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Keywords: Low-level Vision

Publications

2018

FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
Yu Chen*, Ying Tai*, Xiaoming Liu, Chunhua Shen, Jian Yang
In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, Jun. 2018 (Spotlight)
Bibtex | PDF | arXiv | Project Webpage | Code

2017

MemNet: A Persistent Memory Network for Image Restoration
Ying Tai, Jian Yang, Xiaoming Liu, Chunyan Xu
In Proceeding of International Conference on Computer Vision (ICCV 2017), Venice, Italy, Oct. 2017
Bibtex | PDF | Project Webpage | Code

Image Super-Resolution via Deep Recursive Residual Network
Ying Tai, Jian Yang, Xiaoming Liu
In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, HI, Jul. 2017
Bibtex | PDF | Project Webpage | Code