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Title: Sparse signal recovery from modulo observations
Abstract We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a relatively new imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the under-determined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the signal recovery problem under sparsity constraints for the special case to modulo folding limited to two periods. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal. We also provide experiments validating our approach on toy signal and image data and demonstrate its promising performance.
Authors:
;
Award ID(s):
2005804 1815101
Publication Date:
NSF-PAR ID:
10311124
Journal Name:
EURASIP Journal on Advances in Signal Processing
Volume:
2021
Issue:
1
ISSN:
1687-6180
Sponsoring Org:
National Science Foundation
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