Sparse signal processing has emerged to be an exciting area of research with lots of opportunities to develop new results. The key idea behind this field is that many real-world signals admit a parsimonious representation in some basis, which could be incorporated as a prior in solving many inverse problems in signal and image processing, including the very sampling mechanism by which the signal is acquired (which leads to the notion of compressive sensing). Sparsity has played a key role in compression of signals such as speech, electrocardiogram signals, images, video, etc. The recent thrust in the field has been to solve various inverse problems within the framework of sparsity. The sparsity constraint enables one to find unique solutions even in the case of underdetermined system of equations. The field has strong links to convex optimization, machine learning, linear algebra, Bayesian estimation, dictionary learning, etc. In the first workshop of its kind in the area in India, we shall expose the audience to some basic concepts in sparse signal processing to some state-of-the-art developments in the area including important applications in image processing.