156 citations · 210 across the 9 of their papers we have counts for
11 papers · 1 filter
Occlusion-Aware Cost Constructor for Light Field Depth Estimation
Yingqian Wang, Longguang Wang, Zhengyu Liang +3
Matching cost construction is a key step in light field (LF) depth estimation, but was rarely studied in the deep learning era. Recent deep learning-based LF depth estimation metho…
Unsupervised Degradation Representation Learning for Blind Super-Resolution
Longguang Wang, Yingqian Wang, Xiaoyu Dong +4
Most existing CNN-based super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic downsampling). However, these meth…
Symmetric Parallax Attention for Stereo Image Super-Resolution
Yingqian Wang, Xinyi Ying, Longguang Wang +3
Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used. Sin…
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…
DeOccNet: Learning to See Through Foreground Occlusions in Light Fields
Yingqian Wang, Tianhao Wu, Jungang Yang +3
Background objects occluded in some views of a light field (LF) camera can be seen by other views. Consequently, occluded surfaces are possible to be reconstructed from LF images.…