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 …

Currently, light fields play important roles in industry, including in 3D mapping, virtual reality
and other fields. However, as a kind of high-latitude data, light field images are difficult to …

Light fields contain a wealth of information about real-world scenes and can be easily
acquired by commercial light field cameras. However, the trade-off between angular and …

Light Field (LF) imaging enables many attractive applications since scene angular and
spatial information can be captured simultaneously. However, the limited angular resolution …

Light field imaging can record the intensity and direction information of light rays in space,
which attracts extensive attention. However, the trade-off between angular and spatial …

Occlusion removal in an image can aid in facilitating the robustness of numerous computer
vision tasks, eg, detection and tracking in surveillance. However, the invisible property of …

Current low-light light-field (LF) image enhancement algorithms tend to produce blurry
results, for (1) loss of spatial details during enhancement and (2) inefficient exploitation of …

Light field imaging can encode abundant scene information, including the intensity and
direction of light rays, into 4D light field images. However, the limited number of sensors in …

Light Field (LF) imaging can record both the intensities and directions of light rays in a single
exposure, which has received extensive attentions. However, the limited angular resolution …