318 citations · 1.3k across the 17 of their papers we have counts for
12 papers · 1 filter
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution
Zhengyu Liang, Yingqian Wang, Longguang Wang +3
Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution (SR), but is highly challenging due to its non-local property caused by the disparities…
MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection
Tianhao Wu, Boyang Li, Yihang Luo +6
Space-based infrared tiny ship detection aims at separating tiny ships from the images captured by earth orbiting satellites. Due to the extremely large image coverage area (e.g.,…
Real-World Light Field Image Super-Resolution via Degradation Modulation
Yingqian Wang, Zhengyu Liang, Longguang Wang +3
Recent years have witnessed the great advances of deep neural networks (DNNs) in light field (LF) image super-resolution (SR). However, existing DNN-based LF image SR methods are d…
Light Field Image Super-Resolution with Transformers
Zhengyu Liang, Yingqian Wang, Longguang Wang +2
Light field (LF) image super-resolution (SR) aims at reconstructing high-resolution LF images from their low-resolution counterparts. Although CNN-based methods have achieved remar…
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…
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…
Parallax Attention for Unsupervised Stereo Correspondence Learning
Longguang Wang, Yulan Guo, Yingqian Wang +4
Stereo image pairs encode 3D scene cues into stereo correspondences between the left and right images. To exploit 3D cues within stereo images, recent CNN based methods commonly us…
Learning A Single Network for Scale-Arbitrary Super-Resolution
Longguang Wang, Yingqian Wang, Zaiping Lin +3
Recently, the performance of single image super-resolution (SR) has been significantly improved with powerful networks. However, these networks are developed for image SR with a si…
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.…
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…
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution
Yingqian Wang, Longguang Wang, Jungang Yang +2
With the popularity of dual cameras in recently released smart phones, a growing number of super-resolution (SR) methods have been proposed to enhance the resolution of stereo imag…