Seminar: Graduate Seminar
Towards Extended Field-of-View Wavefront Shaping
Date:
June,09,2026
Start Time:
15:30 - 16:30
Location:
506, New Zisapel Building
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Lecturer:
Hadar Cohen
Research Areas:
| Optical imaging of biological tissues at depth presents a significant challenge due to strong light scattering within the tissue, which distorts the wavefront and limits the ability to focus light deep inside the sample. One of the main strategies to address this issue is wavefront shaping (WFS), a powerful technique for deep tissue imaging that physically corrects scattering aberrations to maximize the signal-to-noise ratio. However, its practical utility is severely limited by a highly localized correction field. Current efforts to extend the field of view (FoV) rely on the optical memory effect to tile local corrections, but this two-dimensional approximation degrades rapidly in thick, heterogeneous tissue. In this thesis, we investigate algorithms that could allow WFS corrections to apply to a larger tissue volume. We present a comprehensive roadmap for extended FoV WFS by formulating it as a sparse Transmission Matrix (TM) interpolation problem. Using a rigorous synthetic dataset of 3D tissue structures, we systematically benchmark computational approaches ranging from physical multi-scattering inversions (diffraction tomography) to physics-informed deep learning. We demonstrate that transitioning from 2D memory effects to any 3D volumetric prior yields significant performance gains. Furthermore, we reveal a critical ”generalization gap” in deep wavefront shaping. While physics-informed networks effectively learn visually plausible 3D tissue structures to stabilize TM interpolation in highly scattering regimes, their inductive bias fundamentally struggles to estimate the pseudo-random, high-frequency speckle patterns required for accurate optical phase reconstruction. By characterizing this bottleneck between structural regularization and phase fidelity, our benchmark establishes the necessity of hybrid neuro-physical architectures for generalized deep tissue imaging. |
| MSc student under the supervision of Prof. Anat Levin.
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