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Breaking the coherence barrier - A new theory for compressed sensing
Published on Oct 29, 20142743 Views
This paper provides an extension of compressed sensing which bridges a substantial gap between existing theory and its current use in real-world applications. It introduces a mathematical framework th
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Chapter list
Breaking the coherence barrier - A new theory for compressed sensing00:00
Compressed Sensing in Inverse Problems - 100:23
Compressed Sensing in Inverse Problems - 201:55
Compressed Sensing03:27
Pillars of Compressed Sensing03:34
Uniform Random Subsampling04:51
Sparsity05:53
Sparsity and the Flip Test06:30
Sparsity - The Flip Test - 106:54
Sparsity - The Flip Test - 207:38
Sparsity- The Flip Test: Results08:09
Sparsity - The Flip Test - 308:27
Sparsity - The Flip Test (contd.) - 108:45
Sparsity - The Flip Test (contd.) - 208:57
Lesson Learned!09:10
What about the RIP?09:41
Images are not sparse, they are asymptotically sparse - 110:13
Images are not sparse, they are asymptotically sparse - 211:11
Analog inverse problems are coherent11:52
Analog inverse problems are coherent, why?12:28
Analog inverse problems are asymptotically13:20
We need a new theory14:12
New Pillars of Compressed Sensing15:16
Sparsity in levels - 115:22
Sparsity in levels - 216:03
Multi-level sampling scheme16:17
Local coherence16:52
The optimization problem17:18
Theoretical Results17:27
Fourier to wavelets18:06
r-level Sampling Scheme19:37
Resolution Dependence, 5% subsampling19:58
2048 × 2048 full sampling and 5% subsampling (DB4)20:36
The GLPU-Phantom21:26
Seeing further with compressed sensing - 121:46
Seeing further with compressed sensing - 222:01
Key Question23:01
Siemens23:48
Siemens Conclusion:24:10
Key Question24:50
Other work25:21
Related Papers26:29