Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables

Abstract

Deep learning models have significantly improved the visual quality and accuracy on compressive sensing recovery. In this paper, we propose an algorithm for signal reconstruction from compressed measurements with image priors captured by a generative model. We search and constrain on latent variable space to make the method stable when the number of compressed measurements is extremely limited. We show that, by exploiting certain structures of the latent variables, the proposed method produces improved reconstruction accuracy and preserves realistic and non-smooth features in the image. Our algorithm achieves high computation speed by projecting between the original signal space and the latent variable space in an alternating fashion.

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Cited by in corpus (2)

• Generalization analysis of an unfolding network for analysis-based Compressed Sensing
• Learning Generative Prior with Latent Space Sparsity Constraints