Abstract Inverse methods involving compressive sensing are tested in the application of two‐dimensional aperture‐synthesis imaging of radar backscatter from field‐aligned plasma density irregularities in the ionosphere. We consider basis pursuit denoising, implemented with the fast iterative shrinkage thresholding algorithm, and orthogonal matching pursuit (OMP) with a wavelet basis in the evaluation. These methods are compared with two more conventional optimization methods rooted in entropy maximization (MaxENT) and adaptive beamforming (linearly constrained minimum variance or often “Capon's Method.”) Synthetic data corresponding to an extended ionospheric radar target are considered. We find that MaxENT outperforms the other methods in terms of its ability to recover imagery of an extended target with broad dynamic range. Fast iterative shrinkage thresholding algorithm performs reasonably well but does not reproduce the full dynamic range of the target. It is also the most computationally expensive of the methods tested. OMP is very fast computationally but prone to a high degree of clutter in this application. We also point out that the formulation of MaxENT used here is very similar to OMP in some respects, the difference being that the former reconstructs the logarithm of the image rather than the image itself from basis vectors extracted from the observation matrix. MaxENT could in that regard be considered a form of compressive sensing.
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OpenICS: Open image compressive sensing toolbox and benchmark
The real-world application of image compressive sensing is largely limited by the lack of standardization in implementation and evaluation. To address this limitation, we present OpenICS, an image compressive sensing toolbox that implements multiple popular image compressive sensing algorithms into a unified framework with a standardized user interface. Furthermore, a corresponding benchmark is also proposed to provide a fair and complete evaluation of the implemented algorithms. We hope this work can serve the growing research community of compressive sensing and the industry to facilitate the development and application of image compressive sensing.
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- Award ID(s):
- 1652038
- PAR ID:
- 10313911
- Date Published:
- Journal Name:
- Software impacts
- Volume:
- 9
- Issue:
- 100081
- ISSN:
- 2665-9638
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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