  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
make its solution unique. For each constraint we prove conditions for
uniqueness, and suggest a simple method to retrieve the solution. Under the
uniqueness conditions, and as long as the signals are sparse enough, we
demonstrate through simulations that without knowing the sparsity basis our
methods can achieve results similar to those of standard compressed sensing,
which relay on prior knowledge of the sparsity basis. 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.
