39.1: Invited Paper: Super‐resolution light field display based on learned elemental image and designed compound lens

2021 ◽  
Vol 52 (S2) ◽  
pp. 491-491
Author(s):  
Xubo Yu ◽  
Xinzhu Sang ◽  
Xin Gao ◽  
HanYu Li ◽  
Xiwen Su
Keyword(s):  
Light Field ◽  
Super Resolution ◽  
Elemental Image

Cross-Scale Reference-Based Light Field Super-Resolution

2018 ◽  
Vol 4 (3) ◽  
pp. 406-418 ◽  
Author(s):  
Mandan Zhao ◽  
Gaochang Wu ◽  
Yipeng Li ◽  
Xiangyang Hao ◽  
Lu Fang ◽  
...  
Keyword(s):  
Light Field ◽  
Super Resolution

A Fast View Synthesis Implementation Method for Light Field Applications

2021 ◽  
Vol 17 (4) ◽  
pp. 1-20
Author(s):  
Wei Gao ◽  
Linjie Zhou ◽  
Lvfang Tao
Keyword(s):  
Neural Networks ◽  
Light Field ◽  
Three Dimensional ◽  
Super Resolution ◽  
View Synthesis ◽  
Compact Representation ◽  
Light Weight ◽  
Computationally Efficient ◽  
Full Resolution ◽  
Time Systems

View synthesis (VS) for light field images is a very time-consuming task due to the great quantity of involved pixels and intensive computations, which may prevent it from the practical three-dimensional real-time systems. In this article, we propose an acceleration approach for deep learning-based light field view synthesis, which can significantly reduce calculations by using compact-resolution (CR) representation and super-resolution (SR) techniques, as well as light-weight neural networks. The proposed architecture has three cascaded neural networks, including a CR network to generate the compact representation for original input views, a VS network to synthesize new views from down-scaled compact views, and a SR network to reconstruct high-quality views with full resolution. All these networks are jointly trained with the integrated losses of CR, VS, and SR networks. Moreover, due to the redundancy of deep neural networks, we use the efficient light-weight strategy to prune filters for simplification and inference acceleration. Experimental results demonstrate that the proposed method can greatly reduce the processing time and become much more computationally efficient with competitive image quality.

Light Field Super-Resolution with Zero-Shot Learning

2021 ◽  
Author(s):  
Zhen Cheng ◽  
Zhiwei Xiong ◽  
Chang Chen ◽  
Dong Liu ◽  
Zheng-Jun Zha
Keyword(s):  
Light Field ◽  
Super Resolution
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