Tree-Structure Bayesian Compressive Sensing for Video

Xin Yuan; Patrick Llull; David J. Brady; Lawrence Carin

Tree-Structure Bayesian Compressive Sensing for Video

Xin Yuan, Patrick Llull, David J. Brady, Lawrence Carin

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Abstract

A Bayesian compressive sensing framework is developed for video reconstruction based on the color coded aperture compressive temporal imaging (CACTI) system. By exploiting the three dimension (3D) tree structure of the wavelet and Discrete Cosine Transformation (DCT) coefficients, a Bayesian compressive sensing inversion algorithm is derived to reconstruct (up to 22) color video frames from a single monochromatic compressive measurement. Both simulated and real datasets are adopted to verify the performance of the proposed algorithm.

Original language	Undefined/Unknown
Journal	Arxiv preprint
State	Published - Oct 12 2014
Externally published	Yes

Keywords

cs.CV

Access to Document

1410.3080v1

Cite this

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title = "Tree-Structure Bayesian Compressive Sensing for Video",

abstract = " A Bayesian compressive sensing framework is developed for video reconstruction based on the color coded aperture compressive temporal imaging (CACTI) system. By exploiting the three dimension (3D) tree structure of the wavelet and Discrete Cosine Transformation (DCT) coefficients, a Bayesian compressive sensing inversion algorithm is derived to reconstruct (up to 22) color video frames from a single monochromatic compressive measurement. Both simulated and real datasets are adopted to verify the performance of the proposed algorithm. ",

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AB - A Bayesian compressive sensing framework is developed for video reconstruction based on the color coded aperture compressive temporal imaging (CACTI) system. By exploiting the three dimension (3D) tree structure of the wavelet and Discrete Cosine Transformation (DCT) coefficients, a Bayesian compressive sensing inversion algorithm is derived to reconstruct (up to 22) color video frames from a single monochromatic compressive measurement. Both simulated and real datasets are adopted to verify the performance of the proposed algorithm.

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JF - Arxiv preprint

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