TR2018-008
Accelerated Image Reconstruction for Nonlinear Diffractive Imaging
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- "Accelerated Image Reconstruction for Nonlinear Diffractive Imaging", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP.2018.8462400, April 2018, pp. 6473-6477.BibTeX TR2018-008 PDF Video Software
- @inproceedings{Ma2018apr,
- author = {Ma, Yanting and Mansour, Hassan and Liu, Dehong and Boufounos, Petros T. and Kamilov, Ulugbek},
- title = {Accelerated Image Reconstruction for Nonlinear Diffractive Imaging},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2018,
- pages = {6473--6477},
- month = apr,
- doi = {10.1109/ICASSP.2018.8462400},
- url = {https://www.merl.com/publications/TR2018-008}
- }
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- "Accelerated Image Reconstruction for Nonlinear Diffractive Imaging", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP.2018.8462400, April 2018, pp. 6473-6477.
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MERL Contacts:
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Research Area:
Abstract:
The radar autofocus problem arises in situations where radar measurements are acquired of a scene using antennas that suffer from position ambiguity. Current techniques model the antenna ambiguity as a global phase error affecting the received radar measurement at every antenna. However, the phase error signal model is only valid in the far field regime where the position error can be approximated by a one dimensional shift in the down-range direction. We propose in this paper an alternate formulation where the antenna position error is modeled using a two-dimensional shift operator in the imagedomain. The radar autofocus problem then becomes a multichannel two-dimensional blind deconvolution problem where the static radar image is convolved with a two dimensional shift kernel for each antenna measurement. We develop an alternating minimization framework that leverages the sparsity and piece-wise smoothness of the radar scene, as well as the one-sparse property of the two dimensional shift kernels.
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Related News & Events
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NEWS MERL presenting 9 papers at ICASSP 2018 Date: April 15, 2018 - April 20, 2018
Where: Calgary, AB
MERL Contacts: Petros T. Boufounos; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Philip V. Orlik; Pu (Perry) Wang
Research Areas: Computational Sensing, Digital Video, Speech & AudioBrief- MERL researchers are presenting 9 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held in Calgary from April 15-20, 2018. Topics to be presented include recent advances in speech recognition, audio processing, and computational sensing. MERL is also a sponsor of the conference.
ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.
- MERL researchers are presenting 9 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held in Calgary from April 15-20, 2018. Topics to be presented include recent advances in speech recognition, audio processing, and computational sensing. MERL is also a sponsor of the conference.
Related Video
Related Publication
- @article{Ma2017aug,
- author = {Ma, Yanting and Mansour, Hassan and Liu, Dehong and Boufounos, Petros T. and Kamilov, Ulugbek},
- title = {Accelerated Image Reconstruction for Nonlinear Diffractive Imaging},
- journal = {arXiv},
- year = 2017,
- month = aug,
- url = {https://arxiv.org/abs/1708.01663}
- }