318 citations · 1.1k across the 52 of their papers we have counts for
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Disentangling Light Fields for Super-Resolution and Disparity Estimation
Yingqian Wang, Longguang Wang, Gaochang Wu +4
Light field (LF) cameras record both intensity and directions of light rays, and encode 3D scenes into 4D LF images. Recently, many convolutional neural networks (CNNs) have been p…
Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-Resolution
Xinyi Ying, Yingqian Wang, Longguang Wang +4
Infrared small target super-resolution (SR) aims to recover reliable and detailed high-resolution image with high-contrast targets from its low-resolution counterparts. Since the i…
A Systematic Survey of Deep Learning-based Single-Image Super-Resolution
Juncheng Li, Zehua Pei, Wenjie Li +4
Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and has…
Light Field Image Super-Resolution Using Deformable Convolution
Yingqian Wang, Jungang Yang, Longguang Wang +4
Light field (LF) cameras can record scenes from multiple perspectives, and thus introduce beneficial angular information for image super-resolution (SR). However, it is challenging…
Spatial-Angular Interaction for Light Field Image Super-Resolution
Yingqian Wang, Longguang Wang, Jungang Yang +3
Light field (LF) cameras record both intensity and directions of light rays, and capture scenes from a number of viewpoints. Both information within each perspective (i.e., spatial…