1 / 32

Random Convolution in Compressive Sampling

cascata
Download Presentation

Random Convolution in Compressive Sampling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

    1. Random Convolution in Compressive Sampling Michael Fleyer

    2. Standard Sampling Nyquist/Shannon sampling:

    3. Compressive Sampling

    4. Compressive Sampling (cont.)

    5. CS example (Compressive Sensing Richard Baraniuk Rice University, Lecture Notes in IEEE Signal Processing Magazine Volume 24, July 2007)

    6. Sparsity

    7. Sparsity (cont.)

    8. Incoherence

    9. Incoherence (cont.)

    10. CS-required properties

    11. Sparse signal recovery

    12. Reconstruction conditions

    13. Linear Programming

    14. Example

    15. Robust CS

    16. RIP and CS

    17. General signal recovery

    18. Recovery from noisy signals

    19. Random sensing

    20. Random sensing (cont.)

    21. CS main results

    22. CS by random convolution Compressive Sensing By Random Convolution Justin Romberg, submitted to SIAM Journal on Imaging Science

    23. CS by random convolution (cont.)

    24. CS by random convolution (cont.)

    25. Subsampling

    26. Applications

    27. Applications (cont.)

    28. Applications (cont.)

    29. Coherence bounds

    30. Cumulative coherence

    31. Main Results

    32. Example

More Related