Complex-Valued Signal Recovery using the Bayesian LASSO
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916175732015104 |
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| author | Green, Dylan Lindbloom, Jonathan Gelb, Anne |
| author_facet | Green, Dylan Lindbloom, Jonathan Gelb, Anne |
| contents | Recovering complex-valued image recovery from noisy indirect data is important in applications such as ultrasound imaging and synthetic aperture radar. While there are many effective algorithms to recover point estimates of the magnitude, fewer are designed to recover the phase. Quantifying uncertainty in the estimate can also provide valuable information for real-time decision making. This investigation therefore proposes a new Bayesian inference method that recovers point estimates while also quantifying the uncertainty for complex-valued signals or images given noisy and indirect observation data. Our method is motivated by the Bayesian LASSO approach for real-valued sparse signals, and here we demonstrate that the Bayesian LASSO can be effectively adapted to recover complex-valued images whose magnitude is sparse in some (e.g.~the gradient) domain. Numerical examples demonstrate our algorithm's robustness to noise as well as its computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16992 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Complex-Valued Signal Recovery using the Bayesian LASSO Green, Dylan Lindbloom, Jonathan Gelb, Anne Numerical Analysis 62C10, 62F15, 65C20 Recovering complex-valued image recovery from noisy indirect data is important in applications such as ultrasound imaging and synthetic aperture radar. While there are many effective algorithms to recover point estimates of the magnitude, fewer are designed to recover the phase. Quantifying uncertainty in the estimate can also provide valuable information for real-time decision making. This investigation therefore proposes a new Bayesian inference method that recovers point estimates while also quantifying the uncertainty for complex-valued signals or images given noisy and indirect observation data. Our method is motivated by the Bayesian LASSO approach for real-valued sparse signals, and here we demonstrate that the Bayesian LASSO can be effectively adapted to recover complex-valued images whose magnitude is sparse in some (e.g.~the gradient) domain. Numerical examples demonstrate our algorithm's robustness to noise as well as its computational efficiency. |
| title | Complex-Valued Signal Recovery using the Bayesian LASSO |
| topic | Numerical Analysis 62C10, 62F15, 65C20 |
| url | https://arxiv.org/abs/2403.16992 |