Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters. Ammanouil, R., Ferrari, A., Flamary, R., Ferrari, C., & Mary, D. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 1435-1439, Aug, 2017.
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Paper doi abstract bibtex
Paper doi abstract bibtex As the world's largest radio telescope, the Square Kilometer Array (SKA) will provide radio interferometric data with unprecedented detail. Image reconstruction algorithms for radio interferometry are challenged to scale well with TeraByte image sizes never seen before. In this work, we investigate one such 3D image reconstruction algorithm known as MUFFIN (MUlti-Frequency image reconstruction For radio INterferometry). In particular, we focus on the challenging task of automatically finding the optimal regularization parameter values. In practice, finding the regularization parameters using classical grid search is computationally intensive and nontrivial due to the lack of ground-truth. We adopt a greedy strategy where, at each iteration, the optimal parameters are found by minimizing the predicted Stein unbiased risk estimate (PSURE). The proposed self-tuned version of MUFFIN involves parallel and computationally efficient steps, and scales well with large-scale data. Finally, numerical results on a 3D image are presented to showcase the performance of the proposed approach.
@InProceedings{8081446,
author = {R. Ammanouil and A. Ferrari and R. Flamary and C. Ferrari and D. Mary},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters},
year = {2017},
pages = {1435-1439},
abstract = {As the world's largest radio telescope, the Square Kilometer Array (SKA) will provide radio interferometric data with unprecedented detail. Image reconstruction algorithms for radio interferometry are challenged to scale well with TeraByte image sizes never seen before. In this work, we investigate one such 3D image reconstruction algorithm known as MUFFIN (MUlti-Frequency image reconstruction For radio INterferometry). In particular, we focus on the challenging task of automatically finding the optimal regularization parameter values. In practice, finding the regularization parameters using classical grid search is computationally intensive and nontrivial due to the lack of ground-truth. We adopt a greedy strategy where, at each iteration, the optimal parameters are found by minimizing the predicted Stein unbiased risk estimate (PSURE). The proposed self-tuned version of MUFFIN involves parallel and computationally efficient steps, and scales well with large-scale data. Finally, numerical results on a 3D image are presented to showcase the performance of the proposed approach.},
keywords = {image reconstruction;interferometry;iterative methods;radiotelescopes;search problems;multifrequency image reconstruction;self-tuned regularization parameters;Square Kilometer Array;radio interferometric data;image reconstruction algorithms;radio interferometry;TeraByte image;3D image reconstruction algorithm;optimal regularization parameter values;MUFFIN;classical grid search;predicted Stein unbiased risk estimate;Signal processing algorithms;Jacobian matrices;Deconvolution;Image reconstruction;Radio interferometry;Antenna measurements;Estimation},
doi = {10.23919/EUSIPCO.2017.8081446},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347562.pdf},
}Downloads: 0
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Image reconstruction algorithms for radio interferometry are challenged to scale well with TeraByte image sizes never seen before. In this work, we investigate one such 3D image reconstruction algorithm known as MUFFIN (MUlti-Frequency image reconstruction For radio INterferometry). In particular, we focus on the challenging task of automatically finding the optimal regularization parameter values. In practice, finding the regularization parameters using classical grid search is computationally intensive and nontrivial due to the lack of ground-truth. We adopt a greedy strategy where, at each iteration, the optimal parameters are found by minimizing the predicted Stein unbiased risk estimate (PSURE). The proposed self-tuned version of MUFFIN involves parallel and computationally efficient steps, and scales well with large-scale data. 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