  We consider the problem of recovering a set of correlated signals (e.g.,
images from different viewpoints) from a few linear measurements per signal. We
assume that each sensor in a network acquires a compressed signal in the form
of linear measurements and sends it to a joint decoder for reconstruction. We
propose a novel joint reconstruction algorithm that exploits correlation among
underlying signals. Our correlation model considers geometrical transformations
between the supports of the different signals. The proposed joint decoder
estimates the correlation and reconstructs the signals using a simple
thresholding algorithm. We give both theoretical and experimental evidence to
show that our method largely outperforms independent decoding in terms of
support recovery and reconstruction quality.
