Toeplitz-Structured Compressed Sensing Matrices

By Waheed Uz Zaman Bajwa, Jarvis D. Haupt, Gil M. Raz, Stephen J. Wright, Robert D. Nowak. 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, 2007

341 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f10f69a5607c4ecef995f12688951070beb16c1f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Waheed Uz Zaman Bajwa
2. Jarvis D. Haupt
3. Robert D. Nowak
4. Gil M. Raz
5. Trac Duy Tran
6. Nam H. Nguyen
7. Armando Manduca
8. Thong T. Do

In Computer Vision and Pattern Recognition

1. Jarvis D. Haupt
2. Waheed Uz Zaman Bajwa
3. Gil M. Raz
4. Armando Manduca
5. Thong T. Do
6. Trac Duy Tran
7. Joshua D. Trzasko
8. Nam H. Nguyen

In Signal Processing

1. Jarvis D. Haupt
2. Waheed Uz Zaman Bajwa
3. Gil M. Raz
4. Thong T. Do
5. Trac Duy Tran
6. Nam H. Nguyen
7. Lu Gan
8. Robert D. Nowak

Citations in the corpus

• Highly Undersampled Magnetic Resonance Image Reconstruction via Homotopic $\ell_{0}$ -Minimization (2009, background): “[12]) into the construction of deterministic and structured matrices obeying (7).”
• Toeplitz Compressed Sensing Matrices With Applications to Sparse Channel Estimation (2010, methodology): “The first theoretical results for using random Toeplitz matrices in compressed sensing were established in [15].”
• Fast and Efficient Compressive Sensing Using Structurally Random Matrices (2011, methodology): “Although there are a few other methods that ex ploit the same idea of convolving a signal with a random pulse , for examples: Random Filter in [17] and Toeplitz structured sensing matrix in [18], only the Random Convolution method can be shown to approach optimal sensing performance.”
• Deterministic Designs with Deterministic Guarantees: Toeplitz Compressed Sensing Matrices, Sequence Designs and System Identification (2008, neutral): “Motivated by these issues [1] consider random Toeplitz matrices and derive information theoretic limits while [3, 4] develop RIP properties for random generated Toeplitz matrices.”
• Signal Processing With Compressive Measurements (2010, neutral): “We now observe that we can extend this result beyond a single -dimensional subspace to all possible -dimensional sub-spaces that are defined with respect to an orthonormal basis , i.e., .”
• ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing (2020, neutral): “matrix [65].”