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arxiv: 2606.22648 · v1 · pith:L7KIQXKMnew · submitted 2026-06-21 · ⚛️ physics.optics

Leveraging target dynamics for imaging in complex media

Yoav Ben Haim , Ilay Tomer , Omri Haim , Benzy Laufer , Ori Katz This is my paper

Pith reviewed 2026-06-26 09:39 UTC · model grok-4.3

classification ⚛️ physics.optics
keywords optical imagingscattering compensationtarget dynamicscomplex mediaholographic imagingfluorescence microscopymatrix methods
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The pith

Target dynamics supply the information normally provided by varying illumination patterns, allowing scattering-compensated reconstruction of moving scenes with one acquisition per frame.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a mathematical equivalence between the temporal changes in a dynamic target and the diversity created by multiple controlled illumination patterns. This equivalence lets existing matrix and model-based scattering-compensation methods work on realistic moving targets such as flowing blood without requiring the target to stay still across many frames. A reader would care because conventional approaches demand either stationary targets or fast hardware that is often unavailable in biological samples. The result is that motion, usually treated as an obstacle, becomes the source of the required measurements.

Core claim

The mathematical equivalence between target dynamics and conventional varying illumination patterns allows reconstruction of dynamic scenes with a number of acquisitions equal to the number of reconstructed frames, without the use of any spatial light modulators or illumination control. The approach is demonstrated in both coherent holographic imaging and incoherent fluorescence microscopy using established matrix and model-based scattering-compensation techniques.

What carries the argument

The mathematical equivalence between target dynamics and conventional varying illumination patterns, which substitutes natural temporal variability for controlled illumination diversity in matrix and model-based methods.

If this is right

  • Dynamic scenes can be reconstructed using a number of acquisitions equal to the number of frames.
  • No spatial light modulators or active illumination control are required.
  • The same matrix and model-based techniques apply to both coherent holographic and incoherent fluorescence imaging.
  • Natural target motion becomes the information source instead of an obstacle.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach may allow simpler, lower-cost hardware for imaging in living tissue by removing the need for fast modulators.
  • It could be tested on other naturally varying targets such as moving cells or turbulent fluids to check generality.
  • If the equivalence holds, existing scattering-compensation algorithms could be applied directly to video data without modification for stationarity.

Load-bearing premise

The temporal changes in realistic targets supply enough independent information to replace the diversity normally obtained from multiple controlled illuminations.

What would settle it

A controlled experiment in which the target moves too slowly or too predictably to generate sufficient independent measurements, resulting in incomplete scattering compensation or reconstruction failure even when the number of acquisitions matches the number of frames.

read the original abstract

Optical imaging in complex samples such as biological tissues is fundamentally challenging due to random light scattering that degrades resolution and contrast. When imaging realistic targets that contain natural dynamics such as flowing blood, the temporal variability introduces an additional obstacle, as the leading computational scattering-compensation methods require the target to remain stationary during a multi-frame acquisition process. Here we show that instead of struggling to perform rapid acquisitions, the target dynamics themselves can serve as an intrinsic information source for scattering compensation, replacing multiple controlled illuminations. The mathematical equivalence between target dynamics and conventional varying illumination patterns allows to demonstrate this approach in coherent holographic imaging and incoherent fluorescence microscopy using established matrix and model-based scattering-compensation techniques. Our general framework enables reconstruction of dynamic scenes with a number of acquisitions equal to the number of reconstructed frames, without the use of any spatial light modulators or illumination control.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript claims that natural temporal dynamics of targets (e.g., flowing blood) in complex scattering media can replace controlled illumination diversity for scattering compensation. It asserts a mathematical equivalence between target dynamics and conventional varying illumination patterns, enabling reconstruction of dynamic scenes with acquisitions equal to the number of frames. This is demonstrated using established matrix and model-based techniques in coherent holographic imaging and incoherent fluorescence microscopy, without spatial light modulators or illumination control.

Significance. If the claimed equivalence holds with realistic target dynamics providing sufficient measurement diversity, the approach could simplify in vivo optical imaging setups by removing the need for external illumination modulation hardware. The reuse of established matrix and model-based methods on an intrinsic source of variation, together with the parameter-free character of the framework, would be a notable strength if the spanning property of the dynamics is rigorously established.

major comments (2)
  1. [Abstract] The central claim of mathematical equivalence requires that the time series of target states produces a measurement operator whose rank and conditioning match those obtained from controlled illuminations. The manuscript must supply explicit conditions (statistical independence, spatial support, temporal correlation length) under which this spanning property holds; without them the substitution for illumination diversity remains an unverified assumption.
  2. The demonstrations in the two modalities are asserted to succeed, yet the abstract (and by extension the supporting sections) provides no quantitative comparison of reconstruction error, matrix conditioning, or subspace overlap between the dynamic-target case and the conventional multi-illumination case. These metrics are load-bearing for the claim that the number of acquisitions can equal the number of frames.
minor comments (1)
  1. Notation for the transmission or measurement matrix should be introduced with an equation number on first use to aid readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important aspects needed to strengthen the central claims. We will revise the manuscript to provide explicit conditions for the equivalence and to include quantitative comparisons as requested.

read point-by-point responses

  1. Referee: [Abstract] The central claim of mathematical equivalence requires that the time series of target states produces a measurement operator whose rank and conditioning match those obtained from controlled illuminations. The manuscript must supply explicit conditions (statistical independence, spatial support, temporal correlation length) under which this spanning property holds; without them the substitution for illumination diversity remains an unverified assumption.

    Authors: We agree that a rigorous statement of the conditions is necessary to substantiate the claimed equivalence. In the revised manuscript we will add a new subsection to the theory section that derives the spanning property under explicit assumptions: statistical independence of successive target states (quantified via a minimum correlation threshold), spatial support of the dynamics covering the relevant scattering volume, and an upper bound on the temporal correlation length relative to the acquisition interval. Under these conditions the dynamic measurement operator is shown to achieve full rank and conditioning comparable to the controlled-illumination operator, thereby justifying the substitution. revision: yes

  2. Referee: The demonstrations in the two modalities are asserted to succeed, yet the abstract (and by extension the supporting sections) provides no quantitative comparison of reconstruction error, matrix conditioning, or subspace overlap between the dynamic-target case and the conventional multi-illumination case. These metrics are load-bearing for the claim that the number of acquisitions can equal the number of frames.

    Authors: We concur that quantitative metrics are required to support the assertion that acquisitions equal to the number of frames suffice. The revised manuscript will incorporate new figures and tables that directly compare the dynamic-target and conventional multi-illumination cases in both modalities. These will report reconstruction error (MSE/PSNR), matrix condition numbers, and subspace overlap (principal angles) for the holographic and fluorescence experiments, confirming that the dynamic approach achieves comparable performance when the stated conditions on target dynamics are met. revision: yes

Circularity Check

0 steps flagged

No circularity: established matrix techniques applied to new variation source

full rationale

The paper's core claim is that target dynamics can substitute for controlled illumination diversity in existing scattering-compensation methods (matrix and model-based). The abstract states a 'mathematical equivalence' but does not derive any result by fitting parameters to the target data and then relabeling the fit as a prediction, nor does it rely on self-citations for uniqueness or load-bearing premises. No equations reduce the output to the input by construction; the framework simply re-uses prior techniques on a different source of variation. This is a standard non-circular reframing of an application problem.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the asserted mathematical equivalence between target dynamics and illumination variation; this equivalence is treated as given rather than derived in the abstract. No free parameters, invented entities, or ad-hoc axioms are mentioned.

axioms (1)
  • domain assumption Scattering process is linear, allowing matrix or model-based compensation techniques to apply equally to dynamic targets and varying illuminations.
    Invoked implicitly when stating that established techniques can be used with target dynamics.

pith-pipeline@v0.9.1-grok · 5677 in / 1271 out tokens · 18169 ms · 2026-06-26T09:39:46.412952+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

35 extracted references · 31 canonical work pages

  1. [1]

    Bertolotti, O

    J. Bertolotti, O. Katz, Imaging in complex media.Nature Physics18(9), 1008–1017 (2022), doi:10.1038/s41567-022-01723-8

    work page doi:10.1038/s41567-022-01723-8 2022

  2. [2]

    Vellekoop, A

    I. Vellekoop, A. Mosk, Focusing coherent light through opaque strongly scattering media. Optics Letters32, 2309–2311 (2007), doi:10.1364/OL.32.002309

    work page doi:10.1364/ol.32.002309 2007

  3. [3]

    Gigan,et al., Roadmap on wavefront shaping and deep imaging in complex media.Journal of Physics: Photonics4, 042501 (2022), doi:10.1088/2515-7647/ac76f9

    S. Gigan,et al., Roadmap on wavefront shaping and deep imaging in complex media.Journal of Physics: Photonics4, 042501 (2022), doi:10.1088/2515-7647/ac76f9

    work page doi:10.1088/2515-7647/ac76f9 2022

  4. [4]

    Yeminy, O

    T. Yeminy, O. Katz, Guidestar-free image-guided wavefront shaping.Science advances7(21), eabf5364 (2021), doi:10.1126/sciadv.abf5364

    work page doi:10.1126/sciadv.abf5364 2021

  5. [5]

    Aizik, A

    D. Aizik, A. Levin, Non-invasive and noise-robust light focusing using confocal wavefront shaping.Nature Communications15(1), 5575 (2024), doi:10.1038/s41467-024-49697-w

    work page doi:10.1038/s41467-024-49697-w 2024

  6. [6]

    Monin, M

    S. Monin, M. Alterman, A. Levin, Rapid wavefront shaping using an optical gradient acquisi- tion.Nature Communications17(1), 1537 (2026), doi:10.1038/s41467-025-68259-2

    work page doi:10.1038/s41467-025-68259-2 2026

  7. [7]

    B. Y. Feng,et al., NeuWS: Neural wavefront shaping for guidestar-free imaging through static and dynamic scattering media.Science Advances9(26), eadg4671 (2023), doi:10.1126/sciadv. adg4671

    work page doi:10.1126/sciadv 2023

  8. [8]

    Labeyrie, Attainment of diffraction limited resolution in large telescopes by Fourier analysing speckle patterns in star images.Astronomy and Astrophysics6, 85–87 (1970)

    A. Labeyrie, Attainment of diffraction limited resolution in large telescopes by Fourier analysing speckle patterns in star images.Astronomy and Astrophysics6, 85–87 (1970)

    1970

  9. [9]

    Ayers, M

    G. Ayers, M. Northcott, J. Dainty, Knox-Thompson and triple-correlation imaging through atmospheric turbulence.Journal of the Optical Society of America A5(7), 963–985 (1988), doi:10.1364/JOSAA.5.000963

    work page doi:10.1364/josaa.5.000963 1988

  10. [10]

    Freund, M

    I. Freund, M. Rosenbluh, S. Feng, Memory effects in propagation of optical waves through disordered media.Physical review letters61(20), 2328 (1988), doi:https://doi.org/10.1103/ PhysRevLett.61.238. 17

    1988

  11. [11]

    O. Katz, P. Heidmann, M. Fink, S. Gigan, Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations.Nature Photonics8(10), 784–790 (2014), doi:10.1038/nphoton.2014.189

    work page doi:10.1038/nphoton.2014.189 2014

  12. [12]

    T. Wu, O. Katz, X. Shao, S. Gigan, Single-shot diffraction-limited imaging through scattering layers via bispectrum analysis.Optics Letters41(21), 5003–5006 (2016), doi:10.1364/OL.41. 005003

    work page doi:10.1364/ol.41 2016

  13. [13]

    J. R. Fienup, Reconstruction of an object from the modulus of its Fourier transform.Optics letters3(1), 27–29 (1978), doi:10.1364/OL.3.000027

    work page doi:10.1364/ol.3.000027 1978

  14. [14]

    S. Kang,et al., High-resolution adaptive optical imaging within thick scattering media using closed-loop accumulation of single scattering.Nature Communications8(1), 2157 (2017), doi:10.1038/s41467-017-02117-8

    work page doi:10.1038/s41467-017-02117-8 2017

  15. [15]

    Badon,et al., Distortion matrix concept for deep optical imaging in scattering media

    A. Badon,et al., Distortion matrix concept for deep optical imaging in scattering media. Science Advances6(30), eaay7170 (2020), doi:10.1126/sciadv.aay7170

    work page doi:10.1126/sciadv.aay7170 2020

  16. [16]

    Farah,et al., Synthetic spatial aperture holographic third harmonic generation microscopy

    Y. Farah,et al., Synthetic spatial aperture holographic third harmonic generation microscopy. Optica11(5), 693–705 (2024), doi:10.1364/OPTICA.521088

    work page doi:10.1364/optica.521088 2024

  17. [17]

    S. Kang, S. Yoon, W. Choi, Implementation of reflection matrix microscopy: an algorithm perspective.Journal of Physics: Photonics7(1) (2025), doi:10.1088/2515-7647/adb7af

    work page doi:10.1088/2515-7647/adb7af 2025

  18. [18]

    Lee,et al., High-throughput volumetric adaptive optical imaging using compressed time-reversal matrix.Light: Science & Applications11(1), 16 (2022), doi:10.1038/ s41377-021-00705-4

    H. Lee,et al., High-throughput volumetric adaptive optical imaging using compressed time-reversal matrix.Light: Science & Applications11(1), 16 (2022), doi:10.1038/ s41377-021-00705-4

    2022

  19. [19]

    Weinberg, E

    G. Weinberg, E. Sunray, O. Katz, Noninvasive megapixel fluorescence microscopy through scattering layers by a virtual incoherent reflection matrix.Science Advances10(47), eadl5218 (2024), doi:10.1126/sciadv.adl5218

    work page doi:10.1126/sciadv.adl5218 2024

  20. [20]

    O. Haim, J. Boger-Lombard, O. Katz, Image-guided computational holographic wavefront shaping.Nature Photonics19(1), 44–53 (2025), doi:10.1038/s41566-024-01544-6. 18

    work page doi:10.1038/s41566-024-01544-6 2025

  21. [21]

    Y. Zhang,et al., Adaptive optical multispectral matrix approach for label-free high-resolution imaging through complex scattering media.Advanced Photonics7(4), 046008 (2025), doi: 10.1117/1.AP.7.4.046008

    work page doi:10.1117/1.ap.7.4.046008 2025

  22. [22]

    Efficientsam: Leveraged masked image pretraining for efficient seg- ment anything,

    M. Xie,et al., WaveMo: Learning Wavefront Modulations to See Through Scattering, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(2024), pp. 25276–25285, doi:10.1109/CVPR52733.2024.02388

    work page doi:10.1109/cvpr52733.2024.02388 2024

  23. [23]

    Jiang, H

    W. Jiang, H. Guo, C. A. Metzler, A. Veeraraghavan, Guidestar-Free Adaptive Optics with Asymmetric Apertures.ACM Trans. Graph.(2026), just Accepted, doi:10.1145/3809501

    work page doi:10.1145/3809501 2026

  24. [24]

    Lee, D.-Y

    Y.-R. Lee, D.-Y. Kim, Y. Jo, M. Kim, W. Choi, Exploiting volumetric wave correlation for enhanced depth imaging in scattering medium.Nature Communications14(2023), doi:10. 1038/s41467-023-37467-z

    2023

  25. [25]

    Balondrade,et al., Multi-spectral reflection matrix for ultrafast 3D label-free microscopy

    P. Balondrade,et al., Multi-spectral reflection matrix for ultrafast 3D label-free microscopy. Nature Photonics18(10), 1097–1104 (2024), doi:10.1038/s41566-024-01479-y

    work page doi:10.1038/s41566-024-01479-y 2024

  26. [26]

    Kang,et al., Tracing multiple scattering trajectories for deep optical imaging in scattering media.Nature Communications14(2023), doi:10.1038/s41467-023-42525-7

    S. Kang,et al., Tracing multiple scattering trajectories for deep optical imaging in scattering media.Nature Communications14(2023), doi:10.1038/s41467-023-42525-7

    work page doi:10.1038/s41467-023-42525-7 2023

  27. [27]

    Gupta,et al., Imaging through volumetric scattering media by decoding angular light paths

    K. Gupta,et al., Imaging through volumetric scattering media by decoding angular light paths. arXiv e-prints(2025), doi:10.48550/arXiv.2509.11491

    work page doi:10.48550/arxiv.2509.11491 2025

  28. [28]

    Snieder, K

    R. Snieder, K. Wapenaar, Imaging with ambient noise.Physics Today63(9), 44–49 (2010), doi:10.1063/1.3490500

    work page doi:10.1063/1.3490500 2010

  29. [29]

    Cavity electro-optics in thin-film lithium niobate for efficient microwave- to-optical transduction

    T. Chaigne, B. Arnal, S. Vilov, E. Bossy, O. Katz, Super-resolution photoacoustic imaging via flow-induced absorption fluctuations.Optica4(11), 1397–1404 (2017), doi:10.1364/OPTICA. 4.001397

    work page doi:10.1364/optica 2017

  30. [30]

    Ventalon, J

    C. Ventalon, J. Mertz, Quasi-confocal fluorescence sectioning with dynamic speckle illumina- tion.Optics letters30(24), 3350–3352 (2005), doi:10.1364/OL.30.003350

    work page doi:10.1364/ol.30.003350 2005

  31. [31]

    M. M. Qureshi,et al., In vivo study of optical speckle decorrelation time across depths in the mouse brain.Biomed. Opt. Express8(11), 4855–4864 (2017), doi:10.1364/BOE.8.004855. 19

    work page doi:10.1364/boe.8.004855 2017

  32. [32]

    Dertinger, R

    T. Dertinger, R. Colyer, G. Iyer, S. Weiss, J. Enderlein, Fast, background-free, 3D super- resolution optical fluctuation imaging (SOFI).Proceedings of the National Academy of Sciences 106(52), 22287–22292 (2009), doi:10.1073/pnas.0907866106

    work page doi:10.1073/pnas.0907866106 2009

  33. [33]

    Errico,et al., Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging.Nature527(7579), 499–502 (2015), doi:10.1038/nature16066

    C. Errico,et al., Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging.Nature527(7579), 499–502 (2015), doi:10.1038/nature16066

    work page doi:10.1038/nature16066 2015

  34. [34]

    Sunray, G

    E. Sunray, G. Weinberg, B. Laufer, O. Katz, Matrix-based imaging through dynamic scattering. Nature Communications16(1), 9413 (2025), doi:10.1038/s41467-025-64422-x

    work page doi:10.1038/s41467-025-64422-x 2025

  35. [35]

    J. W. Goodman,Introduction to Fourier optics(Roberts and Company publishers) (2005). Acknowledgments The authors thank Yevgeny Slobodkin and Jeremy Boger-Lombard for their helpful comments on the writing and presentation of this manuscript. Funding:This project was supported by the H2020 European Research Council (101002406). Author contributions:O.K. pro...

    work page doi:10.5281/zenodo.20578800 2005