Blind Compressed Sensing
The fundamental principle underlying compressed sensing is that a signal, which is sparse under some basis representation, can be recovered from a small number of linear measurements. However, prior knowledge of the sparsity basis is essential for the recovery process. This work introduces the concept of blind compressed sensing, which avoids the need to know the sparsity basis in both the sampling and the recovery process. We suggest three possible constraints on the sparsity basis that can be added to the problem in order to guarantee a unique solution. For each constraint, we prove conditions for uniqueness, and suggest a simple method to retrieve the solution. We demonstrate through simulations that our methods can achieve results similar to those of standard compressed sensing, which rely on prior knowledge of the sparsity basis, as long as the signals are sparse enough. This offers a general sampling and reconstruction system that fits all sparse signals, regardless of the sparsity basis, under the conditions and constraints presented in this work.
compressed sensing blind reconstruction dictionary learning sparse representation