Review paper
Compressive image recovery using recurrent generative model
Published: Sep 1, 2017
Abstract
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 can handle global multiplexing in compressive imaging. We perform MAP inference with RIDE using back-propagation to the inputs and projected gradient method. We...
Paper Details
Title
Compressive image recovery using recurrent generative model
Published Date
Sep 1, 2017
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