  Blind super-resolution can be cast as a low rank matrix recovery problem by
exploiting the inherent simplicity of the signal and the low dimensional
structure of point spread functions. In this paper, we develop a simple yet
efficient non-convex projected gradient descent method for this problem based
on the low rank structure of the vectorized Hankel matrix associated with the
target matrix. Theoretical analysis indicates that the proposed method exactly
converges to the target matrix with a linear convergence rate under the similar
conditions as convex approaches. Numerical results show that our approach is
competitive with existing convex approaches in terms of recovery ability and
efficiency.
