# Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables

arXiv: https://arxiv.org/abs/1902.06913 · HTML: https://mlsanity.com/paper/1902.06913/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2019.

## Built on by (most cited first, 5 of 5)

- [Regularising Inverse Problems with Generative Machine Learning Models](https://mlsanity.com/paper/2107.11191/) (2021)
- [A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography](https://mlsanity.com/paper/2211.00002/) (2022)
- [Generalization analysis of an unfolding network for analysis-based Compressed Sensing](https://mlsanity.com/paper/2303.05582/) (2023)
- [Learning Generative Prior with Latent Space Sparsity Constraints](https://mlsanity.com/paper/2105.11956/) (2021)
- [Efficient Compressed Ratio Estimation Using Online Sequential Learning for Edge Computing](https://mlsanity.com/paper/2211.04284/) (2022)

## Abstract (arXiv)

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.