Upcoming Talks

Scattering of light in complex samples, such as biological tissues or fog, renders most samples opaque and presents a fundamental challenge for conventional optical imaging. This is a problem of great practical importance, limiting applications from deep-tissue microscopy to automotive sensing and astronomy [1]. However, although seemingly random, scattering is a deterministic process, meaning that, in principle, the distortion can be undone and a clear image can be recovered. The main challenge is how to find (and apply) the specific complex correction needed in a non-invasive and practical fashion. Recent works have demonstrated that this is indeed achievable in practice: by either physically correcting the distroted wavefronts using spatial light modulators (SLMs) [2], or performing the correction computationally [3-7]. I will review the principles and limitations of the state of the art physics-based techniques for undoing scattering noninvasively. These are based on exploiting the inherent correlations of scattered speckle patterns to decompose the scattering matrix of the sample, without requiring known ‘guide stars’ or any training data. I will also show how natural dynamic fluctuations in the medium or targets, usually considered a hurdle for scattering compensation, can be used, instead of fought against, to undo scattering computationally [5-7]. References [1] Imaging in complex media, J.Bertolotti, O.Katz, Nature Physics, 18, 1008–1017 (2022) [2] Guidestar-free image-guided wavefront shaping, T.Yeminy, O.Katz, Science Advances, 7, 21 (2021). [3] Image-guided Computational Holographic Wavefront Shaping, O.Haim, J.Boger-Lombard, O.Katz, Nature Photonics :2305.12232 (2023). [4] Noninvasive megapixel fluorescence microscopy through scattering layers by a virtual incoherent reflection matrix, G.Weinberg, E.Sunray, O.Katz, Science Advances, 10, 47 (2024). [5] Matrix-based imaging through dynamic scattering, E.Sunary, G.Weinberg, B.Laufer, O.Katz, Nature Communications, 16, 9413 (2025) [6] Leveraging target dynamics for imaging in complex media, Y. Ben Haim et al., arXiv:2606.22648 [7] Leveraging natural fluctuations for matrix-based aberration correction in photoacoustic imaging, Y. Slobodkin, O. Katz, arXiv:2604.27774

Ori Katz is a Professor of Applied Physics at the Hebrew University of Jerusalem. His lab develops physics-based methods for imaging, sensing, and controlling waves in complex media. Beyond imaging, his work on time-reversed lasing was selected as a Top 10 Breakthrough of 2022 by Physics World. He earned his Ph.D. in physics from the Weizmann Institute of Science, where he worked on ultrafast optics, quantum coherent control, and nonlinear microscopy and spectroscopy. He is a recipient of ERC Starting and Consolidator Grants and the Krill Prize.

The century-old problem known as “Phase retrieval” is the problem of recovering a complex signal from the magnitude of its Fourier transform. In the canonical phase retrieval setting, an electromagnetic field with an unknown phase and magnitude distribution undergoes a Fourier transform by propagating through free space or a lens and is then measured by a camera. Since a camera captures only intensity information, the phase retrieval problem must be solved to recover the original phase and magnitude profile. In realistic scenarios, this data may be noisy, and sometimes partial due to active blocking of the zero order diffraction (a common practice in some imaging applications). Current approaches to this problem either have large time complexity, require additional measurements or constraints, are unstable, and do not offer a guarantee for convergence1,2. In this seminar, I will present the latest updates on “Fast Phase Retrieval”3: the first and only algorithm capable of achieving deterministic recovery of a large class of complex objects from their oversampled Fourier transform in a polynomial number of arithmetic operations. Our algorithm achieves this recovery without requiring any manipulation of the object (such as adding a reference beam) nor requiring additional non-Fourier measurements, overcoming the strict limitations of existing algorithms. The stability to noise, O(Nlog(N)) arithmetic complexity and O(N) sample complexity, make it suitable for realistic, large-scale measurements. In many of these realistic scenarios, such as in coherent diffraction imaging, the central Fourier intensities are missing, typically blocked. This hinders the ability to recover the complex object. We develop a framework for recovering the blocked data by exploiting the object support and long range correlations. The same framework can interpolate blocked measurements and denoise measured intensities, even for large realistic datasets. We demonstrate the approach on both simulated and experimental diffraction data. Together, these methods allow, for the first time, fast, deterministic, and scalable recovery of complex objects from large (million pixel), noisy, and incomplete (blocked) Fourier-intensity measurements. References: 1. Jaganathan, K., Eldar, Y. C. & Hassibi, B. Phase retrieval: An overview of recent developments. arXiv:1510.07713. (2015) 2. Shechtman, Y. et al. Phase Retrieval with Application to Optical Imaging: A contemporary overview. IEEE Signal Process. Mag. 32, 87–109 (2015). 3. Brabec, C., Trajtenberg-Mills, S., Daniel, L. & Englund, D. Deterministic fast and stable phase retrieval in multiple dimensions. arXiv [eess.IV] (2024).

Dr. Sivan Trajtenberg Mills is an assistant professor at the EE department in Tel Aviv Univeristy, Israel. Her research is focused on development of optical tools and devices for quantum control. She was a postdoctoral researcher in the Quantum Photonics lab led by prof. Dirk Englund at the Massachusetts Institute of Technology (MIT). She received her Ph.D from Tel Aviv University, studying structured light in second order nonlinear interactions under supervision of prof. Ady Arie. Receiver of the 2025 Alon award for young faculty, Israel.