Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables
This work addresses compressive sensing recovery for image processing, offering incremental improvements in speed and accuracy.
The paper tackles the problem of signal reconstruction from compressed measurements by using a generative model with structured latent variables, achieving improved reconstruction accuracy and preserving realistic, non-smooth features in images.
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.