Geometry-Consistent Light Field Super-Resolution via Graph-Based Regularization

Light field cameras capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post-capture refocus to depth estimation and image-based rendering. However, light field cameras suffer by design from s...

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Veröffentlicht in:IEEE transactions on image processing Jg. 27; H. 9; S. 4207 - 4218
Hauptverfasser: Rossi, Mattia, Frossard, Pascal
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.09.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
camera array
Cameras
Estimation
Field cameras
graph
Image reconstruction
Light
Light field
Linear programming
multi-view system
Regularization
Spatial resolution
super-resolution
Three-dimensional displays
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung:Light field cameras capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post-capture refocus to depth estimation and image-based rendering. However, light field cameras suffer by design from strong limitations in their spatial resolution. Off-the-shelf super-resolution algorithms are not ideal for light field data, as they do not consider its structure. On the other hand, the few super-resolution algorithms explicitly tailored for light field data exhibit significant limitations, such as the need to carry out a costly disparity estimation procedure with sub-pixel precision. We propose a new light field super-resolution algorithm meant to address these limitations. We use the complementary information in the different light field views to augment the spatial resolution of the whole light field at once. In particular, we show that coupling the multi-view approach with a graph-based regularizer, which enforces the light field geometric structure, permits to avoid the need of a precise and costly disparity estimation step. Extensive experiments show that the new algorithm compares favorably to the state-of-the-art methods for light field super-resolution, both in terms of visual quality and in terms of reconstruction error.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2018.2828983