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Conference Publication

Deep learning sparse ternary projections for compressed sensing of images

Host Publication: IEEE Global Conference on Signal and Information Processing

Authors: D. Nguyen, E. Tsiligianni and N. Deligiannis

Publisher: IEEE

Publication Date: Nov. 2017

Number of Pages: 5

ISBN: 978-1-5090-5990-4


Abstract:

Compressed sensing (CS) is a sampling theory that allows reconstructionof sparse (or compressible) signals from an incompletenumber of measurements, using of a sensing mechanism implementedby an appropriate projection matrix. The CS theory isbased on random Gaussian projection matrices, which satisfy recoveryguarantees with high probability however, sparse ternary[0ǃ+1] projections are more suitable for hardware implementation.In this paper, we present a deep learning approach to obtainvery sparse ternary projections for compressed sensing. Our deeplearning architecture jointly learns a pair of a projection matrix and areconstruction operator in an end-to-end fashion. The experimentalresults on real images demonstrate the effectiveness of the proposedapproach compared to state-of-the-art methods, with significantadvantage in terms of complexity.

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Mr. Duc Nguyen

+32 (0)02 629 168

mdnguyen@etrovub.be

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Dr. Evangelia Tsiligianni

+32 (0)02 629 168

etsiligi@etrovub.be

more info

Prof. Dr. Ir. Nikolaos Deligiannis

+32 (0)02 629 168

ndeligia@etrovub.be

more info

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