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 …

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

With the rapid technological advances of the recent years, the practical instances of the
metaverse have become more immersive and engaging than ever before. In their most …

Compared to 2D imaging data, the 4D light field (LF) data retains richer scene's structure
information, which can significantly improve the computer's perception capability, including …

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 …

Since light field simultaneously records spatial information and angular information of light
rays, it is considered to be beneficial for many potential applications, and semantic …

This paper proposes a novel efficient convolutional neural network (CNN) for light field (LF)
spatial super-resolution (SR). Due to the high dimensionality of LF data, it is important to …

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 …

As an aperture-divided computational imaging system, microlens array (MLA)-based light
field (LF) imaging is playing an increasingly important role in computer vision. As the trade …