Cleve Moler demonstrates the MATLAB matrix computation underlying compressed sensing
| Date | Contributor | Description | Rating |
|---|---|---|---|
| 14 Oct 2010 | Linda Webb |
Can compressed sensing drastically reduce the amount of data required to represent signals and images and then restore the originals exactly? |
| Tag | Applied By | Date/Time |
|---|---|---|
| compressive sensing | reeno joseph | 9 Aug 2011 at 11:57am |
| matrix computation | Linda Webb | 14 Oct 2010 at 1:59pm |
| dct | Linda Webb | 14 Oct 2010 at 1:59pm |
| discrete cosine transform | Linda Webb | 14 Oct 2010 at 1:59pm |
| signal restoration | Linda Webb | 14 Oct 2010 at 1:59pm |
| image restoration | Linda Webb | 14 Oct 2010 at 1:59pm |
| linear equation | Linda Webb | 14 Oct 2010 at 1:59pm |
| nonlinear optimization | Linda Webb | 14 Oct 2010 at 1:59pm |
| matrix | Linda Webb | 14 Oct 2010 at 1:59pm |
| signal processing | Linda Webb | 14 Oct 2010 at 1:59pm |
| image processing | Linda Webb | 14 Oct 2010 at 1:59pm |
| nyquist-shannon sampling theorem | Linda Webb | 14 Oct 2010 at 1:59pm |
| matlab | Linda Webb | 14 Oct 2010 at 1:59pm |
| compressive sensing | Linda Webb | 14 Oct 2010 at 1:59pm |
| compressed sensing | Linda Webb | 14 Oct 2010 at 1:59pm |