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 …

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 …

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 …

Despite the success of deep learning algorithms in hyperspectral image (HSI) classification,
most deep learning models require a large amount of labeled data to optimize the numerous …

Light field (LF) image super-resolution (SR) aims at reconstructing high-resolution LF
images from their low-resolution counterparts. Although CNN-based methods have …

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 (ie, spatial …

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 to …

Deep learning provides a new avenue for light field super-resolution (SR). However, the
domain gap caused by drastically different light field acquisition conditions poses a main …

Data augmentation (DA) is an efficient strategy for improving the performance of deep neural
networks. Recent DA strategies have demonstrated utility in single image super-resolution …