Thresholding-based reconstruction of compressed correlated signals

We consider the problem of recovering a set of correlated signals (\emph{e.g.,} images from different viewpoints) from a few linear measurements per signal. We assume that each sensor in a network acquires a compressed signal in the form of linear measurements and sends it to a joint decoder for reconstruction. We propose a novel joint reconstruction algorithm that exploits correlation among underlying signals. Our correlation model considers geometrical transformations between the supports of the different signals. The proposed joint decoder estimates the correlation and reconstructs the signals using a simple thresholding algorithm. We give both theoretical and experimental evidence to show that our method largely outperforms independent decoding in terms of support recovery and reconstruction quality.


Published in:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Presented at:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, March 25-30, 2012
Year:
2012
ISBN:
978-1-4673-0046-9
Keywords:
Laboratories:




 Record created 2011-11-09, last modified 2018-03-17

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