TY - GEN
T1 - Coherent inverse scattering via transmission matrices
T2 - 2017 IEEE International Conference on Computational Photography, ICCP 2017
AU - Metzler, Christopher A.
AU - Sharma, Manoj K.
AU - Nagesh, Sudarshan
AU - Baraniuk, Richard G.
AU - Cossairt, Oliver Strides
AU - Veeraraghavan, Ashok
N1 - Funding Information:
This work was supported in part by DARPA REVEAL grant HR0011-16-C-0028, ONR grant N00014-15-1-2735, ARO grant W911NF-12-1-0407, and the Big-Data Private-Cloud Research Cyberinfrastructure MRIaward funded by NSF under grant CNS-1338099 and by Rice University.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - A transmission matrix describes the input-output relationship of a complex wavefront as it passes through/reflects off a multiple-scattering medium, such as frosted glass or a painted wall. Knowing a medium's transmission matrix enables one to image through the medium, send signals through the medium, or even use the medium as a lens. The double phase retrieval method is a recently proposed technique to learn a medium's transmission matrix that avoids difficult-to-capture interferometric measurements. Unfortunately, to perform high resolution imaging, existing double phase retrieval methods require (1) a large number of measurements and (2) an unreasonable amount of computation. In this work we focus on the latter of these two problems and reduce computation times with two distinct methods: First, we develop a new phase retrieval algorithm that is significantly faster than existing methods, especially when used with an amplitude-only spatial light modulator (SLM). Second, we calibrate the system using a phase-only SLM, rather than an amplitude-only SLM which was used in previous double phase retrieval experiments. This seemingly trivial change enables us to use a far faster class of phase retrieval algorithms. As a result of these advances, we achieve a 100× reduction in computation times, thereby allowing us to image through scattering media at state-of-the-art resolutions. In addition to these advances, we also release the first publicly available transmission matrix dataset. This contribution will enable phase retrieval researchers to apply their algorithms to real data. Of particular interest to this community, our measurement vectors are naturally i.i.d. subgaussian, i.e., no coded diffraction pattern is required.
AB - A transmission matrix describes the input-output relationship of a complex wavefront as it passes through/reflects off a multiple-scattering medium, such as frosted glass or a painted wall. Knowing a medium's transmission matrix enables one to image through the medium, send signals through the medium, or even use the medium as a lens. The double phase retrieval method is a recently proposed technique to learn a medium's transmission matrix that avoids difficult-to-capture interferometric measurements. Unfortunately, to perform high resolution imaging, existing double phase retrieval methods require (1) a large number of measurements and (2) an unreasonable amount of computation. In this work we focus on the latter of these two problems and reduce computation times with two distinct methods: First, we develop a new phase retrieval algorithm that is significantly faster than existing methods, especially when used with an amplitude-only spatial light modulator (SLM). Second, we calibrate the system using a phase-only SLM, rather than an amplitude-only SLM which was used in previous double phase retrieval experiments. This seemingly trivial change enables us to use a far faster class of phase retrieval algorithms. As a result of these advances, we achieve a 100× reduction in computation times, thereby allowing us to image through scattering media at state-of-the-art resolutions. In addition to these advances, we also release the first publicly available transmission matrix dataset. This contribution will enable phase retrieval researchers to apply their algorithms to real data. Of particular interest to this community, our measurement vectors are naturally i.i.d. subgaussian, i.e., no coded diffraction pattern is required.
UR - http://www.scopus.com/inward/record.url?scp=85025426512&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85025426512&partnerID=8YFLogxK
U2 - 10.1109/ICCPHOT.2017.7951483
DO - 10.1109/ICCPHOT.2017.7951483
M3 - Conference contribution
AN - SCOPUS:85025426512
T3 - 2017 IEEE International Conference on Computational Photography, ICCP 2017 - Proceedings
BT - 2017 IEEE International Conference on Computational Photography, ICCP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 May 2017 through 14 May 2017
ER -