  Reconstruction of signals from compressively sensed measurements is an
ill-posed problem. In this paper, we leverage the recurrent generative model,
RIDE, as an image prior for compressive image reconstruction. Recurrent
networks can model long-range dependencies in images and hence are suitable to
handle global multiplexing in reconstruction from compressive imaging. We
perform MAP inference with RIDE using back-propagation to the inputs and
projected gradient method. We propose an entropy thresholding based approach
for preserving texture in images well. Our approach shows superior
reconstructions compared to recent global reconstruction approaches like D-AMP
and TVAL3 on both simulated and real data.
