Phase retrieval via Sparse Wirtinger Flow

Yuan, Ziyang; Wang, Hongxia; Wang, Qi

doi:10.1016/j.cam.2019.01.009

Cited by 40 publications

(48 citation statements)

References 24 publications

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“…Reconstructing the target from Equation (7), where only the intensity of the echo signal is measured, is a typical PR problem [26,27,28,29,30,31,32,33,34,35]. Classic algorithms for solving PR problems include the GS algorithm, the PhaseLift algorithm, the WF algorithm, etc.…”

Section: Target Reconstruction Principle and Swfos Algorithmmentioning

confidence: 99%

“…In order to reduce the number of coding and sampling while increasing the speed of imaging, it is necessary to explore an algorithm that requires fewer samples. Recently, it was confirmed that utilizing the sparse prior information of the target can effectively reduce the samples required by the algorithm [33,34].…”

Section: Target Reconstruction Principle and Swfos Algorithmmentioning

confidence: 99%

“…In addition, nonconvex algorithms based on amplitude cost function, such as the Reshaped Wirtinger Flow (RWF) [29], Truncated Amplitude Flow (TAF) [30], and Reweighted Amplitude Flow (RAF) [31] algorithms, are more significant in reducing the amount of sample because they have non-smooth but simpler loss functions. Further research also includes sparse PR algorithms, such as the Thresholding Wirtinger Flow [32], Sparse Phase Retrieval via Truncated Amplitude (SPARTA) [33] and Sparse Wirtinger Flow (SWF) [34] algorithms, which can accurately recover solutions with a small number of samples utilizing sparse prior.…”

Section: Introductionmentioning

confidence: 99%

“…If the stepsize is too large, the algorithms will diverge easily, while if the stepsize is too small, the algorithms will converge slowly. For the SPARTA, SWF, and Thresholding Wirtinger Flow algorithms, the stepsizes are selected empirically, and the default stepsize functions or stepsize constants are proposed for the measurement vectors that conform to the standard Gaussian random distribution [26,27,28,29,30,31,32,33,34,35]. However, the measurement vectors of PL-TCAI conform to sub-Gaussian random distribution, and the stepsize selection criteria or functions based on the standard Gaussian random distribution are no longer applicable to PL-TCAI.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Phaseless Terahertz Coded-Aperture Imaging for Sparse Target Based on Phase Retrieval Algorithm

Peng

Luo

Deng

et al. 2019

Sensors

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Phaseless terahertz coded-aperture imaging (PL-TCAI) is a novel radar computational imaging method that utilizes the coded aperture and the incoherent detector array to achieve forward-looking and high-resolution imaging without relying on relative motion. In this paper, we propose a more reasonable and compact architecture for the PL-TCAI system and derive the imaging model of PL-TCAI based on the random frequency-hopping signal. Since most phase retrieval algorithms for PL-TCAI utilize only the intensity of echo signals to accurately reconstruct the target, excessive measurement samples are usually required. In order to reduce the number of measurement samples required for imaging, this paper proposes a sparse Wirtinger flow algorithm with optimal stepsize (SWFOS) by using the sparse prior of the target. The specific procedures of the SWFOS algorithm include the support recovery, initialization by truncated spectral method, iteration via gradient descent scheme, hard threshold operation, and stepsize optimization of iteration. Numerical simulations are performed, and the results show that the SWFOS algorithm not only has good performance for the PR problem, but can also sharply reduce the number of measurement samples required for imaging in the PL-TCAI system.

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Section: Target Reconstruction Principle and Swfos Algorithmmentioning

confidence: 99%

Section: Target Reconstruction Principle and Swfos Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Phaseless Terahertz Coded-Aperture Imaging for Sparse Target Based on Phase Retrieval Algorithm

Peng

Luo

Deng

et al. 2019

Sensors

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“…The antenna produces spatially diverse radiation patterns that vary as a function of the frequency sampled over the operational K-band (17.5-26.5 GHz). In [32], the authors use the more recent sparse WF algorithm proposed in [31] that allows to reduce the computational cost of the method.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Aperture Imaging With Intensity-Only Data

Moscoso

Novikov

Papanicolaou

et al. 2020

IEEE Trans. Comput. Imaging

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We consider imaging the reflectivity of scatterers from intensity-only data recorded by a single moving transducer that both emits and receives signals, forming a synthetic aperture. By exploiting frequency illumination diversity, we obtain multiple intensity measurements at each location, from which we determine field cross-correlations using an appropriate phase controlled illumination strategy and the inner product polarization identity. The field cross-correlations obtained this way do not, however, provide all the missing phase information because they are determined up to a phase that depends on the receiver's location. The main result of this paper is an algorithm with which we recover the field cross-correlations up to a single phase that is common to all the data measured over the synthetic aperture, so all the data are synchronized. Thus, we can image coherently with data over all frequencies and measurement locations as if full phase information was recorded.

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