TY - GEN
T1 - Generative patch priors for practical compressive image recovery
AU - Anirudh, Rushil
AU - Lohit, Suhas
AU - Turaga, Pavan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - 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, uncalibrated compressive sensing dataset. The code and models for GPP are available on github. 1.
AB - 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, uncalibrated compressive sensing dataset. The code and models for GPP are available on github. 1.
UR - http://www.scopus.com/inward/record.url?scp=85116159463&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85116159463&partnerID=8YFLogxK
U2 - 10.1109/WACV48630.2021.00258
DO - 10.1109/WACV48630.2021.00258
M3 - Conference contribution
AN - SCOPUS:85116159463
T3 - Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
SP - 2534
EP - 2544
BT - Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
Y2 - 5 January 2021 through 9 January 2021
ER -