Data-Driven Algorithms for Gaussian Measurement Matrix Design in Compressive Sensing

In this paper, we provide two data-driven algorithms for learning compressive sensing measurement matrices with Gaussian entries. In contrast to the ubiquitous i.i.d. Gaussian design, we associate different variances with different signal entries, so that we may utilize training data to focus more e...

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Bibliographic Details
Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 5523 - 5527
Main Authors: Sun, Yang, Scarlett, Jonathan
Format: Conference Proceeding
Language:English
Published: IEEE 23.05.2022
Subjects:
Compressive sensing
data-driven techniques
generative priors
measurement matrix design
Power measurement
Sensors
Signal processing
Signal processing algorithms
Sparse matrices
Training
Training data
ISSN:2379-190X
Online Access:Get full text
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Summary:In this paper, we provide two data-driven algorithms for learning compressive sensing measurement matrices with Gaussian entries. In contrast to the ubiquitous i.i.d. Gaussian design, we associate different variances with different signal entries, so that we may utilize training data to focus more energy on the "most important" parts of the signal. Our first algorithm is based on simple variance-proportional sampling (i.e., place more energy at locations where the signal tends to vary more), and our second overcomes limitations of the first by iteratively up-weighing and down-weighing the variance values according to reconstructions performed on the training signals. Our algorithms enjoy the advantages of being simple and versatile, in the sense of being compatible with a diverse range of signal priors and/or decoding rules. We experimentally demonstrate the effectiveness of our algorithms under both generative priors with gradient-based recovery and sparse priors with ℓ 1 -minimization based recovery.
ISSN:2379-190X
DOI:10.1109/ICASSP43922.2022.9747617