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 …

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

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 …

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 …

Spatial-angular separable convolution (SAS-conv) has been widely used for efficient and
effective 4D light field (LF) feature embedding in different tasks, which mimics a 4D …

The disparity information reflects pixel-wise inter-view correlations among sub-aperture
images (SAIs) of a light field (LF) image. Existing CNN-based methods for LF spatial super …

This paper proposes a new full-reference image quality assessment (IQA) model for
performing perceptual quality evaluation on light field (LF) images, called the spatial and …

We present MVSPlenOctree, a novel approach that can efficiently reconstruct radiance fields
for view synthesis. Unlike previous scene-specific radiance fields reconstruction methods …

Non-local self-similarity has been well exploited in the single image super-resolution task as
an effective prior. However, due to the difficulty of modeling the 4D correspondence globally …