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Updated: Jan 7, 2026

Super-resolution Imaging of the Bacterial Division Machinery

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

Data-driven superresolution imaging in disordered media.

Alexander Christie¹, Matan Leibovich², Miguel Moscoso³

¹Department of Mathematics, Stanford University, Stanford, CA 94305.

Proceedings of the National Academy of Sciences of the United States of America

|January 2, 2026

Summary

This summary is machine-generated.

We developed a new method to estimate Green's functions in scattering media, enabling superresolution imaging. This technique improves resolution beyond homogeneous media by using ambient scattering to enlarge the imaging aperture.

Keywords:

imaging neural networks random media sparse dictionary learning superresolution

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Area of Science:

Wave propagation and imaging in complex media.
Seismic imaging and geophysical exploration.
Acoustic and elastic wave phenomena.

Background:

Estimating Green's functions is crucial for seismic imaging.
Strongly scattering media pose significant challenges for conventional imaging techniques.
Superresolution imaging offers enhanced resolution beyond the diffraction limit.

Purpose of the Study:

To develop a robust methodology for estimating Green's functions in strongly scattering media.
To achieve superresolution imaging by leveraging ambient scattering.
To demonstrate the effectiveness of the proposed method using optimization or neural networks.

Main Methods:

Exploiting large and diverse datasets to estimate ambient medium's Green's functions.
Utilizing conventional optimization methods for imaging.
Employing neural networks for superresolution imaging.
Analyzing the enlargement of the physical imaging aperture due to ambient scattering.

Main Results:

Accurate estimation of Green's functions in strongly scattering media.
Achieved excellent imaging results with resolution superior to homogeneous media.
Demonstrated superresolution phenomenon enabled by ambient scattering.
Validated the methodology using both optimization and neural network approaches.

Conclusions:

The proposed methodology enables accurate Green's function estimation and superresolution imaging in complex media.
Ambient scattering effectively enlarges the imaging aperture, leading to enhanced resolution.
This approach overcomes limitations of traditional time reversal methods for imaging.