@presentation {yeung2013SINBADcsr,
	title = {Compressed sensing, recovery of signals using random Turbo matrices},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Compressed sensing is an emerging technique that allows us to recover an image using far fewer number of measurements than classical sampling techniques. Designing the measurement matrices with certain properties are critical to this task. Gaussian matrices are most commonly used. We discover a new class of random matrices that can outperform the Gaussian matrices when we are in a situation of taking an outrageously small number of samples.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/yeung2013SINBADcsr/yeung2013SINBADcsr.pdf},
	author = {Enrico Au-Yeung and Ozgur Yilmaz and Felix J. Herrmann}
}
@presentation {au-yeung2012SINBADcs,
	title = {Compressed sensing, random Fourier matrix and jitter sampling},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Compressed sensing is an emerging signal processing technique that allows signals to be sampled well below the Nyquist rate, when the signal has a sparse representation in an orthonormal basis. By using a random Fourier matrix or a Gaussian matrix as our measurement matrix, we can reconstruct a signal from far fewer measurements than required by Shannon sampling theorem. In this talk, we will discuss the role of uniform versus jitter sampling, both in a theoretical and practical viewpoint.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/au-yeung2012SINBADcs/au-yeung2012SINBADcs_pres.pdf},
	author = {Enrico Au-Yeung and Hassan Mansour and Ozgur Yilmaz}
}
@presentation {yilmaz2011SINBADcsp,
	title = {Compressed sensing with prior information},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/yilmaz2011SINBADcsp/yilmaz2011SINBADcsp_pres.pdf},
	author = {Ozgur Yilmaz}
}
@conference {saab2007SEGcbp,
	title = {Curvelet-based primary-multiple separation from a Bayesian perspective},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2510-2514},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this abstract, we present a novel primary-multiple separation scheme which makes use of the sparsity of both primaries and multiples in a transform domain, such as the curvelet transform, to provide estimates of each. The proposed algorithm utilizes seismic data as well as the output of a preliminary step that provides (possibly) erroneous predictions of the multiples. The algorithm separates the signal components, i.e., the primaries and multiples, by solving an optimization problem that assumes noisy input data and can be derived from a Bayesian perspective. More precisely, the optimization problem can be arrived at via an assumption of a weighted Laplacian distribution for the primary and multiple coefficients in the transform domain and of white Gaussian noise contaminating both the seismic data and the preliminary prediction of the multiples, which both serve as input to the algorithm. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792988},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/saab07SEGcbp/saab07SEGcbp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/saab07SEGcbp/saab07SEGcbp_pres.pdf},
	author = {Rayan Saab and Deli Wang and Ozgur Yilmaz and Felix J. Herrmann}
}
