Abstract
Synthesizing a densely sampled light field from a single image is highly beneficial for many applications. Moreover, jointly solving both angular and spatial super-resolution problem also introduces new possibilities in light field imaging. The conventional method relies on physical-based rendering and a secondary network to solve the angular super-resolution problem. In addition, pixel-based loss limits the network capability to infer scene geometry globally. In this paper, we show that both super-resolution problems can be solved jointly from a single image by proposing a single end-to-end deep neural network that does not require a physical-based approach. Two novel loss functions based on known light field domain knowledge are proposed to enable the network to consider the relation between sub-aperture images. Experimental results show that the proposed model successfully synthesizes dense high resolution light field and it outperforms the state-of-the-art method in both quantitative and qualitative criteria. The method can be generalized to various scenes, rather than focusing on a particular subject. The synthesized light field can be used as if it has been captured by a light field camera, such as depth estimation and refocusing.
Original language | English |
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Article number | 9119124 |
Pages (from-to) | 112562-112573 |
Number of pages | 12 |
Journal | IEEE Access |
Volume | 8 |
DOIs | |
State | Published - 2020 |
Bibliographical note
Publisher Copyright:© 2013 IEEE.
Keywords
- Deep neural network
- light field
- machine learning
- super-resolution