  Parsimony in signal representation is a topic of active research. Sparse
signal processing and representation is the outcome of this line of research
which has many applications in information processing and has shown significant
improvement in real-world applications such as recovery, classification,
clustering, super resolution, etc. This vast influence of sparse signal
processing in real-world problems raises a significant need in developing novel
sparse signal representation algorithms to obtain more robust systems. In such
algorithms, a few open challenges remain in (a) efficiently posing sparsity on
signals that can capture the structure of underlying signal and (b) the design
of tractable algorithms that can recover signals under aforementioned sparse
models.
