Description: MATLAB implementation of compressive sensing example as described in R.
Baraniuk, Compressive Sensing, IEEE Signal Processing Magazine, [118],
July 2007. The code acquires 250 averaged random measurements of a 2500
pixel image. We assume that the image has a sparse representation in the
DCT domain (not very sparse in practice). Hence the image can be
recovered from its compressed form using basis pursuit.
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File list (Check if you may need any files):
| Filename | Size | Date |
|---|
| Compressive_Sensing.m | 920 | 2011-11-18
|
| license.txt | 1314 | 2014-02-12 |
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