This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at
predicting disparity information of central view image in a light field (ie, pixel offset between …

In this report we summarize the 2nd NTIRE challenge on light field (LF) image super-
resolution (SR) with a focus on new methods and results. This challenge aims at super …

Global context information is particularly important for comprehensive scene understanding.
It helps clarify local confusions and smooth predictions to achieve fine-grained and coherent …

In this report, we summarize the first NTIRE challenge on light field (LF) image super-
resolution (SR), which aims at super-resolving LF images under the standard bicubic …

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 among …

Compared with external-supervised learning-based (ESLB) methods, self-supervised
learning-based (SSLB) methods can overcome the domain gap problem caused by different …

Light field (LF) depth estimation is a crucial basis for LF-related applications. Most existing
methods are based on the Lambertian assumption and cannot deal with non-Lambertian …

Light Field (LF) depth estimation is an important research direction in the area of computer
vision and computational photography, which aims to infer the depth information of different …

Recent efforts have witnessed remarkable progress in satellite video super-resolution
(SVSR). However, most SVSR methods usually assume the degradation is fixed and known …

Light field (LF) semantic segmentation is a newly arisen technology and is widely used in
many smart city applications such as remote sensing, virtual reality and 3D photogrammetry …