.png){kind=icon}
返回
High-Fidelity Light Field Reconstruction Method Using View-Selective Angular Feature Extraction
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3261967.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Deep learning (DL) provides an effective approach for light field (LF) reconstruction that aims to synthesize novel views from sparsely-sampled views. However, it is challenging to address domain asymmetry when adopting spatial-angular interaction LF reconstruction methods. To overcome this problem, a view-selective angular feature extraction block (VS-LFAFE) is proposed to obtain full-resolution angular features that enumerate whole viewpoints in a macropixel. By applying the VS-LFAFE, a novel LF reconstruction method is proposed, consisting of two subblocks: a spatial-angular feature extraction and fusion block, and an angular upsampling block. Experimental results demonstrate the effectiveness of the VS-LFAFE, and validate that the proposed method can achieve superior performance compared with the state-of-the-art methods.
Keyword:
Feature extraction
Image reconstruction
Convolutional neural networks
Reconstruction algorithms
Estimation
Spatial resolution
Interpolation
Light field reconstruction
light field imaging
view-selective angular feature
convolutional neural network
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容