7 citations · 8 across the 4 of their papers we have counts for
6 papers
Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines
Mantang Guo, Jing Jin, Hui Liu +1
In this paper, we tackle the problem of dense light field (LF) reconstruction from sparsely-sampled ones with wide baselines and propose a learnable model, namely dynamic interpola…
Deep Spatial-angular Regularization for Compressive Light Field Reconstruction over Coded Apertures
Mantang Guo, Junhui Hou, Jing Jin +2
Coded aperture is a promising approach for capturing the 4-D light field (LF), in which the 4-D data are compressively modulated into 2-D coded measurements that are further decode…
Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization
Jing Jin, Junhui Hou, Jie Chen +1
Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution as the limited sampling resources have to be shared with the angular dimension. LF…
Learning Light Field Angular Super-Resolution via a Geometry-Aware Network
Jing Jin, Junhui Hou, Hui Yuan +1
The acquisition of light field images with high angular resolution is costly. Although many methods have been proposed to improve the angular resolution of a sparsely-sampled light…
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware Fusion
Jing Jin, Junhui Hou, Jie Chen +3
A densely-sampled light field (LF) is highly desirable in various applications, such as 3-D reconstruction, post-capture refocusing and virtual reality. However, it is costly to ac…
Light Field Super-resolution via Attention-Guided Fusion of Hybrid Lenses
Jing Jin, Junhui Hou, Jie Chen +2
This paper explores the problem of reconstructing high-resolution light field (LF) images from hybrid lenses, including a high-resolution camera surrounded by multiple low-resoluti…