# Generative Patch Priors for Practical Compressive Image Recovery

arXiv: https://arxiv.org/abs/2006.10873 · HTML: https://mlsanity.com/paper/2006.10873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches](https://mlsanity.com/paper/2203.05134/) (2022)

## Abstract (arXiv)

In this paper, we propose the generative patch prior (GPP) that defines a generative prior for compressive image recovery, based on patch-manifold models. Unlike learned, image-level priors that are restricted to the range space of a pre-trained generator, GPP can recover a wide variety of natural images using a pre-trained patch generator. Additionally, GPP retains the benefits of generative priors like high reconstruction quality at extremely low sensing rates, while also being much more generally applicable. We show that GPP outperforms several unsupervised and supervised techniques on three different sensing models -- linear compressive sensing with known, and unknown calibration settings, and the non-linear phase retrieval problem. Finally, we propose an alternating optimization strategy using GPP for joint calibration-and-reconstruction which performs favorably against several baselines on a real world, un-calibrated compressive sensing dataset.