arXiv subjects

Dennis Scheidt

Publications and source records attributed to Dennis Scheidt.

5 recordsLinked to original sources

arXiv2026

3D scattered light imaging: extracting 3D fiber orientations from 1D line profiles in brain imaging

Understanding the 3D fiber architecture of the brain at the microscopic scale is essential for revealing its structural connectivity and function. Polarization-based optical imaging (3D-PLI) techniques enable high-fidelity reconstruction of single nerve fiber orientations but struggle to resolve fiber crossings, which are critical for recovering the full connectome. Scattering-based imaging provides access to the structure factor of three-dimensionally oriented fibers. By probing a fixed scattering angle under multiple azimuthal illumination angles, in-plane fiber orientations and crossings can be recovered using computational scattered light imaging (SLI). However, despite containing 3D information, a theoretical framework to extract full 3D orientations has been lacking. In this talk, a simple analogical approximation of Rayleigh-Gans scattering theory is introduced to extract the 3D orientation of nerve fibers from one-dimensional SLI measurements. The theory is validated using tilted microscopic glass phantoms consisting of 2 um-thick rod lattices fabricated by two-photon lithography. Finally, the method is applied to brain tissue samples and compared with 3D-PLI.

physics.optics↗

arXiv2026

Single Pixel Imaging and Compressive Sensing: A Practical Tutorial

Single Pixel Imaging is an emerging imaging technique that employs a bucket detector (photodiode) to sample a spatially modulated light field, rather than measuring the spatial distribution with an array of detectors. This approach provides a low-cost alternative for imaging at unconventional wavelengths and enables improved signal collection in noisy measurement environments. Furthermore, it allows the application of compressive sensing to reduce the amount of acquired data and measurement time, facilitating live or in vivo imaging applications. This tutorial presents the experimental implementation of measurement bases and compressive sensing reconstruction methods, including both deterministic algorithms and deep learning approaches. Accompanying Python notebooks guide readers through the reproduction of the presented results and support the application of the methods to their own work.

physics.optics↗

arXiv2024

Errors in single pixel photography emerging from light collection limits by the bucket detector

In single pixel photography an image is sampled with a programmable optical element like a digital micromirror array or a spatial light modulator that can project an orthogonal base. The light reflected or diffracted is collected by a lens and measured with a photodiode (bucket detector). In this work we demonstrate that single pixel photography that uses sampling bases with non-zero off-diagonal elements (i.e. Hadamard), can be susceptible to errors that emerge from the relative size of the bucket detector area compared with the spatial spread of the Fourier spectrum of the base element that has the the highest spatial frequency. Experiments with a spatial light modulator and simulations using a Hadamard basis show that if the bucket detector area is smaller than between $50-75\%$ of the maximum area spanned by the projected spectrum of the measurement basis, the reconstructed photograph will exhibit cross-talk with the effective phase of the optical system. The phase can be encoded or errors can be introduced in the optical system to demonstrate this effect.

physics.optics↗

arXiv2023

Shaping the angular spectrum of a Bessel beam to enhance light transfer through dynamic strongly-scattering media

We prepare a quasi-non-diffracting Bessel beam defined within an annular angular spectrum with a spatial light modulator. The beam propagates though a strongly scattering media and the transmitted speckle pattern is measured at one point with a Hadamard Walsh basis that divides the ring into $N$ segments ($N=16,64,256, 1024$). The phase of the transmitted beam is reconstructed with 3 step interferometry and the intensity of the transmitted beam is optimized by projecting the conjugate phase at the SLM. We find that the optimum intensity is attained for the condition that the transverse wave vector $k_\perp$ (of the Bessel beam) matches the spatial azimuthal frequencies of the segmented ring $k_ϕ$. Furthermore, compared with beams defined on a 2d grid (i.e. Gaussian) a reasonable enhancement is achieved for all the $k_\perp$ sampled with only 64 elements. Finally, the measurements can be done while the scatterer is moving as long as the total displacement during the measurement is smaller than the speckle correlation distance.

physics.optics↗

arXiv2022

Comparison between Hadamard and canonical bases for in-situ wavefront correction and the effect of ordering in compressive sensing

In this work we compare the Canonical and Hadamard bases for in-situ wavefront correction of a focused Gaussian beam using a spatial light modulator (SLM). The beam is perturbed with a transparent optical element (sparse) or a random scatterer (both prevent focusing at a single spot). The phase corrections are implemented with different basis sizes ($N=64, 256, 1024, 4096$) and the phase contribution of each basis element is measured with 3 step interferometry. The field is reconstructed from the complete $3N$ measurements and the correction is implemented by projecting the conjugate phase at the SLM. Our experiments show that in general, the Hadamard basis measurements yield better corrections because every element spans the relevant area of the SLM, reducing the noise in the interferograms. % In contrast, the canonical basis has the fundamental limitation that the area of the elements is proportional to $1/N$, and requires dimensions that are compatible with the spatial period of the grating. In the case of the random scatterer, we were only able to get reasonable corrections with the Hadamard basis and the intensity of the corrected spot increased monotonically with $N$, which is consistent with fast random changes in phase over small spatial scales. We also explore compressive sensing with the Hadamard basis and find that the minimum compression ratio needed to achieve corrections with similar quality to those that use the complete measurements depend on the basis ordering. The best results are reached in the case of the Hadamard-Walsh and cake cutting orderings. Surprisingly, in the case of the random scatterer we find that moderate compression ratios on the order of $10-20\%$ ($N=4096$) allow to recover focused spots, although as expected, the maximum intensities increase monotonically with the number of measurements due to the non sparsity of the signal.

eess.IV↗