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Proceedings Paper

Recovery of quantized compressed sensing measurements
Author(s): Grigorios Tsagkatakis; Panagiotis Tsakalides
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Paper Abstract

Compressed Sensing (CS) is a novel mathematical framework that has revolutionized modern signal and image acquisition architectures ranging from one-pixel cameras, to range imaging and medical ultrasound imaging. According to CS, a sparse signal, or a signal that can be sparsely represented in an appropriate collection of elementary examples, can be recovered from a small number of random linear measurements. However, real life systems may introduce non-linearities in the encoding in order to achieve a particular goal. Quantization of the acquired measurements is an example of such a non-linearity introduced in order to reduce storage and communications requirements. In this work, we consider the case of scalar quantization of CS measurements and propose a novel recovery mechanism that enforces the constraints associated with the quantization processes during recovery. The proposed recovery mechanism, termed Quantized Orthogonal Matching Pursuit (Q-OMP) is based on a modification of the OMP greedy sparsity seeking algorithm where the process of quantization is explicit considered during decoding. Simulation results on the recovery of images acquired by a CS approach reveal that the modified framework is able to achieve significantly higher reconstruction performance compared to its naive counterpart under a wide range of sampling rates and sensing parameters, at a minimum cost in computational complexity.

Paper Details

Date Published: 12 March 2015
PDF: 9 pages
Proc. SPIE 9401, Computational Imaging XIII, 940106 (12 March 2015); doi: 10.1117/12.2083285
Show Author Affiliations
Grigorios Tsagkatakis, Foundation for Research and Technology-Hellas (Greece)
Panagiotis Tsakalides, Foundation for Research and Technology-Hellas (Greece)
Univ. of Crete (Greece)


Published in SPIE Proceedings Vol. 9401:
Computational Imaging XIII
Charles A. Bouman; Ken D. Sauer, Editor(s)

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