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FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES

Citation Author(s):
Submitted by:
Shaojie Xu
Last updated:
12 May 2019 - 12:59pm
Document Type:
Presentation Slides
Document Year:
2019
Event:
Presenters Name:
Shaojie Xu
Paper Code:
4829

Abstract 

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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Dataset Files

Xu, Shaojie ICCASP 2019 Presentation Slides.pdf

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