Iterative, backscatter-analysis algorithms for increasing transmission and focusing light through highly-scattering random media
arXiv:1303.3617 · doi:10.1364/JOSAA.30.001592
Abstract
Scattering hinders the passage of light through random media and consequently limits the usefulness of optical techniques for sensing and imaging. Thus, methods for increasing the transmission of light through such random media are of interest. Against this backdrop, recent theoretical and experimental advances have suggested the existence of a few highly transmitting eigen-wavefronts with transmission coefficients close to one in strongly backscattering random media. Here, we numerically analyze this phenomenon in 2-D with fully spectrally accurate simulators and provide rigorous numerical evidence confirming the existence of these highly transmitting eigen-wavefronts in random media with periodic boundary conditions that is composed of hundreds of thousands of non-absorbing scatterers. Motivated by bio-imaging applications where it is not possible to measure the transmitted fields, we develop physically realizable algorithms for increasing the transmission through such random media using backscatter analysis. We show via numerical simulations that the algorithms converge rapidly, yielding a near-optimum wavefront in just a few iterations. We also develop an algorithm that combines the knowledge of these highly transmitting eigen-wavefronts obtained from backscatter analysis, with intensity measurements at a point to produce a near-optimal focus with significantly fewer measurements than a method that does not utilize this information.
Published version
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Cited by in corpus (4)
- Light fields in complex media: mesoscopic scattering meets wave control
- Light scattering control in transmission and reflection with neural networks
- Backscatter analysis based algorithms for increasing transmission through highly-scattering random media using phase-only modulated wavefronts
- The transmission coefficient distribution of highly scattering sparse random media