Signal Reconstruction from Modulo Observations
This addresses a problem in imaging and signal processing for extending dynamic range, but it is incremental as it builds on prior work in phase retrieval and sparsity constraints.
The paper tackles the problem of reconstructing a signal from under-determined modulo observations, which is challenging and ill-posed, by proposing a novel algorithm that perfectly recovers the underlying signal given sufficient measurements and shows improved performance over existing methods.
We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known 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 inverse problem limited to two modulo periods, inspired by recent advances in algorithms for phase retrieval under sparsity constraints. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal and provides improved performance over other existing algorithms. We also provide experiments validating our approach on both synthetic and real data to depict its superior performance.