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CLIM SIGNED

Computational Light fields IMaging

Total Cost €

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EC-Contrib. €

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Partnership

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Project "CLIM" data sheet

The following table provides information about the project.

Coordinator
INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE 

Organization address
address: DOMAINE DE VOLUCEAU ROCQUENCOURT
city: LE CHESNAY CEDEX
postcode: 78153
website: www.inria.fr

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Project website http://clim.inria.fr
 Total cost 2˙461˙086 €
 EC max contribution 2˙461˙086 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-AdG
 Funding Scheme ERC-ADG
 Starting year 2016
 Duration (year-month-day) from 2016-09-01   to  2021-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE FR (LE CHESNAY CEDEX) coordinator 2˙461˙086.00

Map

 Project objective

Light fields technology holds great promises in computational imaging. Light fields cameras capture light rays as they interact with physical objects in the scene. The recorded flow of rays (the light field) yields a rich description of the scene enabling advanced image creation capabilities from a single capture. This technology is expected to bring disruptive changes in computational imaging. However, the trajectory to a deployment of light fields remains cumbersome. Bottlenecks need to be alleviated before being able to fully exploit its potential. Barriers that CLIM addresses are the huge amount of high-dimensional (4D/5D) data produced by light fields, limitations of capturing devices, editing and image creation capabilities from compressed light fields. These barriers cannot be overcome by a simple application of methods which have made the success of digital imaging in past decades. The 4D/5D sampling of the geometric distribution of light rays striking the camera sensors imply radical changes in the signal processing chain compared to traditional imaging systems.

The ambition of CLIM is to lay new algorithmic foundations for the 4D/5D light fields processing chain, going from representation, compression to rendering. Data processing becomes tougher as dimensionality increases, which is the case of light fields compared to 2D images. This leads to the first challenge of CLIM that is the development of methods for low dimensional embedding and sparse representations of 4D/5D light fields. The second challenge is to develop a coding/decoding architecture for light fields which will exploit their geometrical models while preserving the structures that are critical for advanced image creation capabilities. CLIM targets ground-breaking solutions which should open new horizons for a number of consumer and professional markets (photography, augmented reality, light field microscopy, medical imaging, particle image velocimetry).

 Publications

year authors and title journal last update
List of publications.
2019 Reuben A. Farrugia, Christine Guillemot
A simple framework to leverage state-of-the-art single-image super-resolution methods to restore light fields
published pages: 115638, ISSN: 0923-5965, DOI: 10.1016/j.image.2019.115638
Signal Processing: Image Communication Sept. 2019 2019-10-07
2019 Mikael Le Pendu, Christine Guillemot, Aljosa Smolic
A Fourier Disparity Layer Representation for Light Fields
published pages: 5740-5753, ISSN: 1057-7149, DOI: 10.1109/TIP.2019.2922099
IEEE Transactions on Image Processing 28/11 2019-09-13
2019 Mira Rizkallah, Xin Su, Thomas Maugey, Christine Guillemot
Geometry-Aware Graph Transforms for Light Field Compact Representation
published pages: 1-1, ISSN: 1057-7149, DOI: 10.1109/tip.2019.2928873
IEEE Transactions on Image Processing 07/2019 2019-09-13
2019 Jinglei Shi, Xiaoran Jiang, Christine Guillemot
A Framework for Learning Depth From a Flexible Subset of Dense and Sparse Light Field Views
published pages: 5867-5880, ISSN: 1057-7149, DOI: 10.1109/tip.2019.2923323
IEEE Transactions on Image Processing 28/12 2019-09-13
2019 Chiara Galdi, Valeria Chiesa, Christoph Busch, Paulo Lobato Correia, Jean-Luc Dugelay, Christine Guillemot
Light Fields for Face Analysis
published pages: 2687, ISSN: 1424-8220, DOI: 10.3390/s19122687
Sensors 19/12 2019-09-13
2019 Pierre Allain, Laurent Guillo, Christine Guillemot
4D Anisotropic Diffusion Framework with PDEs for Light Field Regularization and Inverse Problems
published pages: 1-1, ISSN: 2333-9403, DOI: 10.1109/tci.2019.2919229
IEEE Transactions on Computational Imaging 06/2019 2019-09-13
2018 Mikael Le Pendu, Xiaoran Jiang, Christine Guillemot
Light Field Inpainting Propagation via Low Rank Matrix Completion
published pages: 1981-1993, ISSN: 1057-7149, DOI: 10.1109/TIP.2018.2791864
IEEE Transactions on Image Processing 27/4 2019-06-13
2017 Christine Guillemot and Reuben Farrugia
Light field image processing: overview and research issues
published pages: , ISSN: , DOI:
MMTC Communications - Frontiers vol. 12, No. 4, July 2017 2019-06-13
2017 Xiaoran Jiang, Mikael Le Pendu, Reuben A. Farrugia, Christine Guillemot
Light Field Compression With Homography-Based Low-Rank Approximation
published pages: 1132-1145, ISSN: 1932-4553, DOI: 10.1109/JSTSP.2017.2747078
IEEE Journal of Selected Topics in Signal Processing 11/7 2019-06-13
2017 Reuben A. Farrugia, Christian Galea, Christine Guillemot
Super Resolution of Light Field Images Using Linear Subspace Projection of Patch-Volumes
published pages: 1058-1071, ISSN: 1932-4553, DOI: 10.1109/JSTSP.2017.2747127
IEEE Journal of Selected Topics in Signal Processing 11/7 2019-06-13
2017 Oriel Frigo and Christine Guillemot
Epipolar Plane Diffusion: An Efficient Approach for Light Field Editing
published pages: , ISSN: , DOI:
British Machine Vision Conference (BMVC) Sept. 2017 2019-06-13
2018 Guillo , Laurent; Jiang , Xiaoran; Lafruit , Gauthier; Guillemot , Christine
Light field video dataset captured by a R8 Raytrix camera (with disparity maps)
published pages: , ISSN: , DOI:
https://hal.inria.fr/hal-01804578 Apr. 2018 2019-04-18
2019 Reuben Farrugia, Christine Guillemot
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks
published pages: 1-1, ISSN: 0162-8828, DOI: 10.1109/tpami.2019.2893666
IEEE Transactions on Pattern Analysis and Machine Intelligence January 2019 2019-04-18

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