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Image-guided computational holographic wavefront shaping

Nature Photonics volume 19, pages 44–53 (2025)Cite this article

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An Author Correction to this article was published on 06 November 2024

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Abstract

Optical imaging through scattering media is important in a variety of fields ranging from microscopy to autonomous vehicles. Although advanced wavefront shaping techniques have offered several breakthroughs in the past decade, current techniques still require a known guide star and a high-resolution spatial light modulator or a very large number of measurements and are limited in their correction field of view. Here we introduce a guide-star-free, non-invasive approach that can correct more than 190,000 scattered modes using only 25 incoherently compounded, holographically measured, scattered light fields, obtained under unknown random illuminations. This is achieved by computationally emulating an image-guided wavefront shaping experiment, where several virtual spatial light modulators are simultaneously optimized to maximize the reconstructed image quality. Our method shifts the burden from the physical hardware to a digital, naturally parallelizable computational optimization, leveraging state-of-the-art automatic differentiation tools. We demonstrate the flexibility and generality of this framework by applying it to imaging through various complex samples and imaging modalities, including epi-illumination, anisoplanatic multi-conjugate correction of highly scattering layers, lensless endoscopy in multicore fibres and acousto-optic tomography. The presented approach offers high versatility, effectiveness and generality for fast, non-invasive imaging in diverse applications.

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Fig. 1: Image-guided computational holographic wavefront shaping.
Fig. 2: Experimental coherence-gated imaging of a complex, layered target through a highly scattering diffuser in reflection.
Fig. 3: Experimental imaging through a highly scattering sample in transmission comparing corrections using different image quality metrics.
Fig. 4: Multi-conjugate anisoplanatic correction through a layered multiple-scattering sample.
Fig. 5: Computational-image-guided lensless endoscopy through MCFs.

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Data availability

Measurement data for Fig. 3 are available via figshare at https://doi.org/10.6084/m9.figshare.23790264 (ref. 55).

Code availability

The core optimization code is available via figshare at https://doi.org/10.6084/m9.figshare.23790264 (ref. 55) and via GitHub at https://github.com/Imaging-Lab-HUJI/Image-guided-Computational-Holographic-Wavefront-Shaping.

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Acknowledgements

We thank G. Weinberg, Y. Slobodkin and E. Sunray for fruitful discussions and M. Rosenfeld for providing the acousto-optic tomography data. This work has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant no. 101002406). This research was supported by a scholarship sponsored by the Ministry of Innovation, Science & Technology, Israel.

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  1. These authors contributed equally: Omri Haim, Jeremy Boger-Lombard.

Authors and Affiliations

  1. Institute of Applied Physics, The Hebrew University of Jerusalem, Jerusalem, Israel

    Omri Haim, Jeremy Boger-Lombard & Ori Katz

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  1. Omri Haim
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Contributions

O.K. conceived the idea. O.K., O.H. and J.B.-L. designed the experimental setup. O.H. and J.B.-L. performed the numerical simulations and data analysis. O.H. designed and implemented the automatic-differentiation-based gradient-ascent algorithm. J.B.-L. led the experimental work. J.B.-L. and O.H. performed the experiments and analysed the data under the supervision of O.K. All authors wrote the manuscript.

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Correspondence to Ori Katz.

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The authors declare no competing interests.

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Nature Photonics thanks Alexandre Aubry, Monika Ritsch-Marte, Ashok Veeraraghavan and Ivo Vellekoop for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Sections 1–11 and Figs. 1–12.

Supplementary Video 1

Evolution of the reconstruction process over iterations, yielding the data in Fig. 2b–d (time gate 1 data).

Supplementary Video 2

Evolution of the reconstruction process over iterations, yielding the data in Fig. 2e–g (time gate 2 data).

Supplementary Video 3

Phase-corrected fields propagated to the USAF-1951 target plane, revealing the transmission image from light weakly reflected by the onion skin slice (Fig. 2h).

Supplementary Video 4

Comparison of corrected and uncorrected incoherently compounded fields from time gate 1 data (Fig. 2) as they are digitally refocused from the scattering layer to the plane of the target object.

Supplementary Video 5

Optimization of multi-conjugate reconstruction with two virtual SLM phase masks, achieving anisoplanatic correction across a wide FoV.

Supplementary Video 6

Phase and amplitude images of 100 reconstructed complex fields corresponding to the results in Fig. 3c, demonstrating access to the complex field at the object plane.

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Haim, O., Boger-Lombard, J. & Katz, O. Image-guided computational holographic wavefront shaping. Nat. Photon. 19, 44–53 (2025). https://doi.org/10.1038/s41566-024-01544-6

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