Evidence map›Paper›PMID 41337601›Full record

ArticleScience advances2025

Quantum-inspired computational wavefront shaping enables turbulence-resilient distributed aperture synthesis imaging.

Shuai Sun, Zhen-Wu Nie, Yao-Kun Xu, Chen Chang, Ping-Xing Chen, Pu-Xiang Lai, Wei-Tao Liu

Abstract read
In one paragraph

Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Shuai SunCollege of Science, National University of Defense Technology, Changsha 410073, China.ORCID 0000-0003-2701-6494
Zhen-Wu NieCollege of Science, National University of Defense Technology, Changsha 410073, China.
Yao-Kun XuCollege of Electronic Science, National University of Defense Technology, Changsha 410073, China.ORCID 0000-0003-4752-762X
Chen ChangCollege of Science, National University of Defense Technology, Changsha 410073, China.
Ping-Xing ChenCollege of Science, National University of Defense Technology, Changsha 410073, China.ORCID 0000-0002-0714-1231
Pu-Xiang LaiDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China.ORCID 0000-0003-4811-2012
Wei-Tao LiuCollege of Science, National University of Defense Technology, Changsha 410073, China.ORCID 0000-0001-8480-9904

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wavefront shaping is essential for optical imaging through aberrations, but conventional methods rely on physical modulators and iterative optimization, hindering real-time applications in dynamic environments like turbulence. Inspired by quantum nonlocal aberration cancellation, we propose a modulator-free, computational wavefront shaping technique. By leveraging classical correlated illumination and single-pixel detection, our method corrects aberrations via virtual phase modulation in the computational domain, eliminating physical spatial light modulators or array sensors. As validation, we demonstrate this approach in a distributed optical aperture synthesis imaging, where a phase-randomized laser array illuminates objects through turbulence. Despite unknown subsource phase mismatch and turbulent distortion, we reconstruct diffraction-limited images of a 3-meter standoff object, at the theoretical resolution limit of the synthetic aperture (0.157 millimeter experimentally; 97% of the 0.152-millimeter limit). This work transforms traditionally intractable hardware challenges into computationally solvable problems, enabling turbulence-resilient standoff imaging without adaptive optics.

Identifiers

PMID41337601
PMCPMC12674157

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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.