Light field reconstruction using shearlet transform in tensorflow

Yuan Gao; Reinhard Koch; Robert Bregovic; Atanas Gotchev

doi:10.1109/ICMEW.2019.00116

Light field reconstruction using shearlet transform in tensorflow

Yuan Gao, Reinhard Koch, Robert Bregovic, Atanas Gotchev

Computing Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

39 Downloads (Pure)

Abstract

Shearlet Transform (ST) is one of the most effective approaches for light field reconstruction from Sparsely-Sampled Light Fields (SSLFs). This demo paper presents a comprehensive implementation of ST for light field reconstruction using one of the most popular machine learning libraries, i.e. Tensor Flow. The flexible architecture of TensorFlow allows for the easy deployment of ST across different platforms (CPUs, GPUs, TPUs) running varying operating systems with high efficiency and accuracy.

Original language	English
Title of host publication	2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019
Publisher	IEEE
ISBN (Electronic)	9781538692141
DOIs	https://doi.org/10.1109/ICMEW.2019.00116
Publication status	Published - 1 Jul 2019
Publication type	A4 Article in conference proceedings
Event	IEEE International Conference on Multimedia and Expo Workshops - Shanghai, China Duration: 8 Jul 2019 → 12 Jul 2019

Conference

Conference	IEEE International Conference on Multimedia and Expo Workshops
Country/Territory	China
City	Shanghai
Period	8/07/19 → 12/07/19

Keywords

Epipolar-Plane Image
Light Field Reconstruction
Light Field Sparsification
Shearlet Transform
TensorFlow

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Media Technology
Computer Vision and Pattern Recognition

Access to Document

10.1109/ICMEW.2019.00116

icme2019w_253-submittedAccepted author manuscript, 550 KB

http://urn.fi/URN:NBN:fi:tuni-202001211439

Cite this

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title = "Light field reconstruction using shearlet transform in tensorflow",

abstract = "Shearlet Transform (ST) is one of the most effective approaches for light field reconstruction from Sparsely-Sampled Light Fields (SSLFs). This demo paper presents a comprehensive implementation of ST for light field reconstruction using one of the most popular machine learning libraries, i.e. Tensor Flow. The flexible architecture of TensorFlow allows for the easy deployment of ST across different platforms (CPUs, GPUs, TPUs) running varying operating systems with high efficiency and accuracy.",

keywords = "Epipolar-Plane Image, Light Field Reconstruction, Light Field Sparsification, Shearlet Transform, TensorFlow",

author = "Yuan Gao and Reinhard Koch and Robert Bregovic and Atanas Gotchev",

year = "2019",

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day = "1",

doi = "10.1109/ICMEW.2019.00116",

language = "English",

booktitle = "2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019",

publisher = "IEEE",

note = "IEEE International Conference on Multimedia and Expo Workshops ; Conference date: 08-07-2019 Through 12-07-2019",

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TY - GEN

T1 - Light field reconstruction using shearlet transform in tensorflow

AU - Gao, Yuan

AU - Koch, Reinhard

AU - Bregovic, Robert

AU - Gotchev, Atanas

PY - 2019/7/1

Y1 - 2019/7/1

N2 - Shearlet Transform (ST) is one of the most effective approaches for light field reconstruction from Sparsely-Sampled Light Fields (SSLFs). This demo paper presents a comprehensive implementation of ST for light field reconstruction using one of the most popular machine learning libraries, i.e. Tensor Flow. The flexible architecture of TensorFlow allows for the easy deployment of ST across different platforms (CPUs, GPUs, TPUs) running varying operating systems with high efficiency and accuracy.

AB - Shearlet Transform (ST) is one of the most effective approaches for light field reconstruction from Sparsely-Sampled Light Fields (SSLFs). This demo paper presents a comprehensive implementation of ST for light field reconstruction using one of the most popular machine learning libraries, i.e. Tensor Flow. The flexible architecture of TensorFlow allows for the easy deployment of ST across different platforms (CPUs, GPUs, TPUs) running varying operating systems with high efficiency and accuracy.

KW - Epipolar-Plane Image

KW - Light Field Reconstruction

KW - Light Field Sparsification

KW - Shearlet Transform

KW - TensorFlow

U2 - 10.1109/ICMEW.2019.00116

DO - 10.1109/ICMEW.2019.00116

M3 - Conference contribution

BT - 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019

PB - IEEE

T2 - IEEE International Conference on Multimedia and Expo Workshops

Y2 - 8 July 2019 through 12 July 2019

ER -

Light field reconstruction using shearlet transform in tensorflow

Abstract

Conference

Keywords

Publication forum classification

ASJC Scopus subject areas

Access to Document

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Cite this