10 citations · 20 across the 4 of their papers we have counts for
6 papers
Exploring Sparsity in Image Super-Resolution for Efficient Inference
Longguang Wang, Xiaoyu Dong, Yingqian Wang +4
Current CNN-based super-resolution (SR) methods process all locations equally with computational resources being uniformly assigned in space. However, since missing details in low-…
Deep Video Super-Resolution using HR Optical Flow Estimation
Longguang Wang, Yulan Guo, Li Liu +3
Video super-resolution (SR) aims at generating a sequence of high-resolution (HR) frames with plausible and temporally consistent details from their low-resolution (LR) counterpart…
Learning Parallax Attention for Stereo Image Super-Resolution
Longguang Wang, Yingqian Wang, Zhengfa Liang +4
Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint. However, it is challenging to i…
Learning for Video Super-Resolution through HR Optical Flow Estimation
Longguang Wang, Yulan Guo, Zaiping Lin +2
Video super-resolution (SR) aims to generate a sequence of high-resolution (HR) frames with plausible and temporally consistent details from their low-resolution (LR) counterparts.…
Fast single image super-resolution based on sigmoid transformation
Longguang Wang, Zaiping Lin, Jinyan Gao +2
Single image super-resolution aims to generate a high-resolution image from a single low-resolution image, which is of great significance in extensive applications. As an ill-posed…
Multi-frame image super-resolution with fast upscaling technique
Longguang Wang, Zaiping Lin, Xinpu Deng +1
Multi-frame image super-resolution (MISR) aims to fuse information in low-resolution (LR) image sequence to compose a high-resolution (HR) one, which is applied extensively in many…