  We address the problem of super-resolution of point sources from binary
measurements, where random projections of the blurred measurement of the actual
signal are encoded using only the sign information. The threshold used for
binary quantization is not known to the decoder. We develop an algorithm that
solves convex programs iteratively and achieves signal recovery. The proposed
algorithm, which we refer to as the binary super-resolution (BSR) algorithm,
recovers point sources with reasonable accuracy, albeit up to a scale factor.
We show through simulations that the BSR algorithm is successful in recovering
the locations and the amplitudes of the point sources, even in the presence of
significant amount of blurring. We also propose a framework for handling noisy
measurements and demonstrate that BSR gives a reliable reconstruction
(correspondingly, reconstruction signal-to-noise ratio (SNR) of about 22 dB)
for a measurement SNR of 15 dB.
