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Towards learning-based denoising of light fields

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Light ﬁeld panorama by a plenoptic camera

Martin Vetterli, Paolo Prandoni, Loïc Arnaud Baboulaz, Zhou Xue

Consumer-grade plenoptic camera Lytro draws a lot of interest from both academic and industrial world. However its low resolution in both spatial and angular domain prevents it from being used for ﬁne and detailed light ﬁeld acquisition. This paper propose ...

SPIE2013

Light Field Compressive Sensing In Camera Arrays

Pierre Vandergheynst, Mohammad Golbabaee, Mahdad Hosseini Kamal

This paper presents a novel approach to capture light field in camera arrays based on the compressive sensing framework. Light fields are captured by a linear array of cameras with overlapping field of view. In this work, we design a redundant dictionary t ...

Ieee2012

Light Field Compressive Sensing in Camera Arrays

Pierre Vandergheynst, Mohammad Golbabaee, Mahdad Hosseini Kamal

Ieee2012

Light Field Compressive Sensing

Pierre Vandergheynst, Mohammad Golbabaee, Mahdad Hosseini Kamal

2012

Light Field Tensor Recovery

Pierre Vandergheynst, Mahdad Hosseini Kamal

This paper presents a novel approach to capture light field in camera arrays based on the tensor recovery framework. Light fields are acquired by a linear array of cameras with overlapping field of view. We represent the light fields in shape of a tensor t ...

2012

Light Field Compressive Sensing in Camera Arrays

Mahdad Hosseini Kamal

This paper presents a novel approach to capture light field in cam- era arrays based on the compressive sensing framework. ...

2012

Virtual View Generation with a Hybrid Camera Array

Engin Tola, Zeyu Zhang

Virtual view synthesis from an array of cameras has been an essential element of three-dimensional video broadcasting/conferencing. In this paper, we propose a scheme based on a hybrid camera array consisting of four regular video cameras and one time-of-f ...

2009

Compressive Sensing for Background Subtraction

Volkan Cevher

Compressive sensing (CS) is an emerging field that provides a framework for image recovery using sub-Nyquist sampling rates. The CS theory shows that a signal can be reconstructed from a small set of random projections, provided that the signal is sparse i ...

2008