5 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.CV2022★ 1 cited
Detail-Preserving Transformer for Light Field Image Super-Resolution
Shunzhou Wang, Tianfei Zhou, Yao Lu +1
Recently, numerous algorithms have been developed to tackle the problem of light field super-resolution (LFSR), i.e., super-resolving low-resolution light fields to gain high-resol…
cs.CV2021★ 1 cited
A Decomposition Model for Stereo Matching
Chengtang Yao, Yunde Jia, Huijun Di +2
In this paper, we present a decomposition model for stereo matching to solve the problem of excessive growth in computational cost (time and memory cost) as the resolution increase…
cs.CV2020★ 5 cited
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching
Chengtang Yao, Yunde Jia, Huijun Di +2
Cost aggregation is a key component of stereo matching for high-quality depth estimation. Most methods use multi-scale processing to downsample cost volume for proper context infor…