Evidence map›Paper›PMID 41481440›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

Data-driven superresolution imaging in disordered media.

Alexander Christie, Matan Leibovich, Miguel Moscoso, Alexei Novikov, George Papanicolaou, Chrysoula Tsogka

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Alexander Christie *Department of Mathematics, Stanford University, Stanford, CA 94305.
Matan Leibovich *Courant Institute of Mathematical Sciences, New York University, New York, NY 10012.
Miguel Moscoso *Department of Mathematics, Universidad Carlos III de Madrid, Leganes, Madrid 28911, Spain.ORCID 0000-0001-8461-1578
Alexei Novikov *Department of Mathematics, The Pennsylvania State University, University Park, PA 16802.
George Papanicolaou *Department of Mathematics, Stanford University, Stanford, CA 94305.
Chrysoula Tsogka *Department of Applied Mathematics, University of California, Merced, CA 95343.ORCID 0000-0002-5839-8889

Funding

AFOSR FA9550-23-1-0352AFOSR FA9550-23-1-0523AFOSR FA9550-24-1-0191Spanish AEI PID2020-115088RB-I00
6 · The paper itself

Abstract

We propose a methodology that exploits large and diverse datasets to accurately estimate the ambient medium's Green's functions in strongly scattering media. Given these estimates, excellent imaging results are achieved, with a resolution that is better than that of a homogeneous medium. This phenomenon, known as superresolution, arises because the ambient scattering medium effectively enlarges the physical imaging aperture. While superresolution has been demonstrated and analyzed extensively in the context of physical time reversal, time reversal itself is not imaging. Our proposed methodology, based on either conventional optimization methods or neural networks, makes it possible to achieve superresolution imaging in complex media.

Indexed as

imagingneural networksrandom mediasparse dictionary learningsuperresolution

Identifiers

PMID41481440
PMCPMC12773735

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.