Compressed Sensing (CS) framework which is linked with the sparse recovery problem has been recently introduced and applied to solve numerous problems.  Measurement matrix has a key role in the CS to sample the signal/images. It has been recently shown that optimization of this matrix can increase the quality of reconstruction.  In this talk we first introduce the CS theory.  Then, the advantages of the measurement matrix optimization and our proposed strategies for this purpose are discussed.  

Finally, we review some applications and extensions of CS such as functional Magnetic Resonance Imaging (fMRI) and Watermarking.