An Architecture for Compressive Imaging

By Michael B. Wakin, Jason N. Laska, Marco F. Duarte, Dror Baron, Shriram Sarvotham, Dharmpal Takhar, Kevin F. Kelly, Richard Baraniuk. International Conference on Information Photonics, 2006

373 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/99ada4d929316b6b82fe56061daf095ccb766c85

How researchers see this paper

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

In Biomedical Engineering

1. Marco F. Duarte
2. Richard Baraniuk
3. Chengbo Li
4. Ramalingam Chellappa
5. Namrata Vaswani
6. Pablo A. Parrilo
7. Pavan K. Turaga
8. Miles J. Padgett

In Computational Mechanics

1. B. Recht
2. Richard Baraniuk
3. Marco F. Duarte
4. E.J. Candes
5. Francis R. Bach
6. Chengbo Li
7. Justin K. Romberg
8. Yonina C. Eldar

In Radiology, Nuclear Medicine and Imaging

1. Chengbo Li
2. Maryam Fazel
3. Richard Baraniuk
4. Pablo A. Parrilo
5. Namrata Vaswani
6. Marco F. Duarte
7. Ramalingam Chellappa
8. B. Recht

Citations in the corpus

• An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing (2010, methodology): “A detailed discussion on TV models has been reported by Chambolle et al. [25, 26].”
• ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements (2016, background): “Motivation: The advances in compressive sensing theory [8, 3, 4] (for the benefit of the readers, a brief background on CS is provided later in the section) has led to the development of many novel imaging devices [23, 27].”
• Single-pixel imaging 12 years on: a review. (2020, background): “Compressive sensing (CS) [3,35,36] has been shown to be a route for exploiting the redundancy in the structure of most natural signals or images.”
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization (2007, background): “ the mnrequired to transmit the values of all the entries. Consider a given image, whose associated matrix Mhas low-rank, or can be well-approximated by a low-rank matrix. As proposed by Wakin et al. [54], a single-pixel camera would ideally produce measurements that are random linear combinations o”
• Kalman filtered Compressed Sensing (2008, background): “INTRODUCTION We consider the problem of reconstructing time sequences ofspatially sparsesignals (with unknown and time-varying sparsity patterns) from alimited numberof linear “incoherent” measurements, in real-time.”
• Compressive sensing: From theory to applications, a survey (2013, neutral): “Block diagram of compressive imaging camera [54].”
• Compressive Sensing for Background Subtraction (2008, background): “Recently, a prototype single pixel camera (SPC) was proposed based on the new mathematical theory of compressive sensing (CS) [4].”