177 citations · 795 across the 20 of their papers we have counts for
20 papers
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…
Dense Dual-Attention Network for Light Field Image Super-Resolution
Yu Mo, Yingqian Wang, Chao Xiao +2
Light field (LF) images can be used to improve the performance of image super-resolution (SR) because both angular and spatial information is available. It is challenging to incorp…
Selective Light Field Refocusing for Camera Arrays Using Bokeh Rendering and Superresolution
Yingqian Wang, Jungang Yang, Yulan Guo +2
Camera arrays provide spatial and angular information within a single snapshot. With refocusing methods, focal planes can be altered after exposure. In this letter, we propose a li…
Dense Nested Attention Network for Infrared Small Target Detection
Boyang Li, Chao Xiao, Longguang Wang +5
Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds. With the advances of deep learning, CNN-based methods have yielded p…
Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection
Ting Liu, Jungang Yang, Boyang Li +4
Infrared small target detection is an important fundamental task in the infrared system. Therefore, many infrared small target detection methods have been proposed, in which the lo…
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…