TY - GEN
T1 - BM3D-PRGAMP
T2 - 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
AU - Metzler, Christopher A.
AU - Maleki, Arian
AU - Baraniuk, Richard G.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/22
Y1 - 2016/9/22
N2 - The explosion of computational imaging has seen the frontier of image processing move past linear problems, like denoising and deblurring, and towards non-linear problems such as phase retrieval. There has a been a corresponding research thrust into non-linear image recovery algorithms, but in many ways this research is stuck where linear problem research was twenty years ago: Models, if used at all, are simple designs like sparsity or smoothness. In this paper we use denoisers to impose elaborate and accurate models in order to perform inference on generalized linear systems. More specifically, we use the state-of-the-art BM3D denoiser within the Generalized Approximate Message Passing (GAMP) framework to solve compressive phase retrieval. Our method demonstrates recovery performance equivalent to existing techniques using fewer than half as many measurements. This dramatic improvement in compressive phase retrieval performance opens the door for a whole new class of imaging systems.
AB - The explosion of computational imaging has seen the frontier of image processing move past linear problems, like denoising and deblurring, and towards non-linear problems such as phase retrieval. There has a been a corresponding research thrust into non-linear image recovery algorithms, but in many ways this research is stuck where linear problem research was twenty years ago: Models, if used at all, are simple designs like sparsity or smoothness. In this paper we use denoisers to impose elaborate and accurate models in order to perform inference on generalized linear systems. More specifically, we use the state-of-the-art BM3D denoiser within the Generalized Approximate Message Passing (GAMP) framework to solve compressive phase retrieval. Our method demonstrates recovery performance equivalent to existing techniques using fewer than half as many measurements. This dramatic improvement in compressive phase retrieval performance opens the door for a whole new class of imaging systems.
KW - Compressive Phase Retrieval
KW - Denoising
KW - Generalized Approximate Message Passing
UR - http://www.scopus.com/inward/record.url?scp=84992146658&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992146658&partnerID=8YFLogxK
U2 - 10.1109/ICMEW.2016.7574718
DO - 10.1109/ICMEW.2016.7574718
M3 - Conference contribution
AN - SCOPUS:84992146658
T3 - 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
BT - 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 July 2016 through 15 July 2016
ER -