@conference {mansour2012ICASSadapt,
	title = {Adaptive compressed sensing for video acquisition},
	booktitle = {ICASSP},
	year = {2012},
	abstract = {In this paper, we propose an adaptive compressed sensing scheme that utilizes a support estimate to focus the measurements on the large valued coefficients of a compressible signal. We embed a "sparse-filtering" stage into the measurement matrix by weighting down the contribution of signal coefficients that are outside the support estimate. We present an application which can benefit from the proposed sampling scheme, namely, video compressive acquisition. We demonstrate that our proposed adaptive CS scheme results in a significant improvement in reconstruction quality compared with standard CS as well as adaptive recovery using weighted $\ell_1$ minimization.},
	keywords = {ICASSP},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICASSP/2012/MansourYilmazICASSPaCS/MansourYilmazICASSPaCS.pdf},
	author = {Hassan Mansour and Ozgur Yilmaz}
}
@conference {aravkin2012ICASSPfastseis,
	title = {Fast seismic imaging for marine data},
	booktitle = {ICASSP},
	year = {2012},
	publisher = {ICASSP},
	organization = {ICASSP},
	abstract = {Seismic imaging can be formulated as a linear inverse problem where a medium perturbation is obtained via minimization of a least-squares misfit functional. The demand for higher resolution images in more geophysically complex areas drives the need to develop techniques that handle problems of tremendous size with limited computational resources. While seismic imaging is amenable to dimensionality reduction techniques that collapse the data volume into a smaller set of "super-shots", these techniques break down for complex acquisition geometries such as marine acquisition, where sources and receivers move during acquisition. To meet these challenges, we propose a novel method that combines sparsity-promoting (SP) solvers with random subset selection of sequential shots, yielding a SP algorithm that only ever sees a small portion of the full data, enabling its application to very large-scale problems. Application of this technique yields excellent results for a complicated synthetic, which underscores the robustness of sparsity promotion and its suitability for seismic imaging.},
	keywords = {ICASSP},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICASSP/2012/AravkinLiHerrmann/AravkinLiHerrmann.pdf},
	author = {Aleksandr Y. Aravkin and Xiang Li and Felix J. Herrmann}
}
@conference {aravkin2012ICASSProbustb,
	title = {Robust inversion via semistochastic dimensionality reduction},
	booktitle = {ICASSP},
	year = {2012},
	pages = {5245-5248},
	publisher = {ICASSP},
	organization = {ICASSP},
	abstract = {We consider a class of inverse problems where it is possible to aggregate the results of multiple experiments. This class includes problems where the forward model is the solution operator to linear ODEs or PDEs. The tremendous size of such problems motivates the use dimensionality reduction (DR) techniques based on randomly mixing experiments. These techniques break down, however, when robust data-fitting formulations are used, which are essential in cases of missing data, unusually large errors, and systematic features in the data unexplained by the forward model. We survey robust methods within a statistical framework, and propose a sampling optimization approach that allows DR. The efficacy of the methods are demonstrated for a large-scale seismic inverse problem using the robust Student{\textquoteright}s t-distribution, where a useful synthetic velocity model is recovered in the extreme scenario of 60\% corrupted data. The sampling approach achieves this recovery using 20\% of the effort required by a direct robust approach.},
	keywords = {ICASSP},
	doi = {10.1109/ICASSP.2012.6289103},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICASSP/2012/AravkinFriedlanderLeeuwen/AravkinFriedlanderLeeuwen.pdf},
	author = {Aleksandr Y. Aravkin and Michael P. Friedlander and Tristan van Leeuwen}
}
@conference {mansour2012ICASSsupport,
	title = {Support driven reweighted $\ell_1$ minimization},
	booktitle = {ICASSP},
	year = {2012},
	abstract = {In this paper, we propose a support driven reweighted $\ell_1$ minimization algorithm (SDRL1) that solves a sequence of weighted $\ell_1$ problems and relies on the support estimate accuracy. Our SDRL1 algorithm is related to the IRL1 algorithm proposed by Candes, Wakin, and Boyd. We demonstrate that it is sufficient to find support estimates with good accuracy and apply constant weights instead of using the inverse coefficient magnitudes to achieve gains similar to those of IRL1. We then prove that given a support estimate with sufficient accuracy, if the signal decays according to a specific rate, the solution to the weighted $\ell_1$ minimization problem results in a support estimate with higher accuracy than the initial estimate. We also show that under certain conditions, it is possible to achieve higher estimate accuracy when the intersection of support estimates is considered. We demonstrate the performance of SDRL1 through numerical simulations and compare it with that of IRL1 and standard $\ell_1$ minimization.},
	keywords = {ICASSP},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICASSP/2012/MansourYilmazICASSPwL1/MansourYilmazICASSPwL1.pdf},
	author = {Hassan Mansour and Ozgur Yilmaz}
}
@conference {saab2008ICASSPssa,
	title = {Stable sparse approximations via nonconvex optimization},
	booktitle = {ICASSP},
	year = {2008},
	publisher = {ICASSP},
	organization = {ICASSP},
	abstract = {We present theoretical results pertaining to the ability of lp minimization to recover sparse and compressible signals from incomplete and noisy measurements. In particular, we extend the results of Cande{\textquoteleft}s, Romberg and Tao [1] to the p < 1 case. Our results indicate that depending on the restricted isometry constants (see, e.g.,[2] and [3]) and the noise level, lp minimization with certain values of p < 1 provides better theoretical guarantees in terms of stability and robustness than l1 minimization does. This is especially true when the restricted isometry constants are relatively large.},
	keywords = {ICASSP},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICASSP/2008/saab08ICASSPssa/saab08ICASSPssa.pdf},
	author = {Rayan Saab and Rick Chartrand and Ozgur Yilmaz}
}
@conference {fomel2007ICASSPrepro,
	title = {Reproducible computational experiments using scons},
	booktitle = {ICASSP},
	year = {2007},
	publisher = {ICASSP},
	organization = {ICASSP},
	abstract = {SCons (from Software Construction) is a well-known open- source program designed primarily for building software. In this paper, we describe our method of extending SCons for managing data processing flows and reproducible computational experiments. We demonstrate our usage of SCons with a simple example.},
	keywords = {ICASSP},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICASSP/2007/fomel07ICASSPrepro/fomel07ICASSPrepro.pdf},
	author = {Sergey Fomel and Gilles Hennenfent}
}
