MATLAB implementation of compressive sensing example as described in R.Baraniuk, Compressive Sensing, IEEE Signal Processing Magazine, , 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.
Helpfull,thank you very much,sir.
Roy, the matrix equation is solved using the MATLAB toolbox l_1-MAGIC: Recovery of Sparse Signals via Convex Programming v1.11 by J. Candes and J. Romberg, Caltech, 2005. Please refer to the authors for details. p.s. This reference was provided in my code comments. You can download the MATLAB toolbox l_1-MAGIC software from here: http://statweb.stanford.edu/~candes/software.html
No information on l1 toolbox. How do you actually reconstruct.
Download apps, toolboxes, and other File Exchange content using Add-On Explorer in MATLAB.