Generative Patch Priors for Practical Compressive Image Recovery

TL;DR

This paper introduces the generative patch prior (GPP), a novel approach for compressive image recovery that leverages patch-manifold models to improve reconstruction quality across various sensing scenarios.

Contribution

GPP is a new generative prior based on patch models that outperforms existing methods in diverse compressive sensing tasks, including calibration and phase retrieval.

Findings

GPP achieves superior reconstruction quality at low sensing rates.

GPP outperforms existing unsupervised and supervised methods across multiple sensing models.

The alternating optimization with GPP effectively handles un-calibrated real-world data.

Abstract

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.