Conference Agenda

Despite significant advances in optical microscopy, no single instrument currently enables super-resolution imaging of the same sample in both intensity and phase modalities simultaneously. This limitation restricts the comprehensiveness of sample analysis, as fluorescence-based super-resolution and quantitative phase imaging (QPI) provide complementary yet distinct information about specimens under study. Fluorescence super-resolution techniques allow visualization of specifically labeled structures beyond the diffraction limit, offering high molecular specificity. Quantitative phase imaging, in contrast, provides label-free, nanometer-scale sensitivity to optical path length variations, revealing morphological and dynamical properties of transparent samples without exogenous contrast agents. Integrating both modalities into a unified platform would enable a more complete characterization of biological and physical samples.

We propose a self-guided quantum phase estimation protocol based on a squeezed vacuum state and a stochastic optimization algorithm. Our protocol dynamically aligns the measurement basis with the target phase, eliminating the need for prior knowledge of the squeezing parameter. We demonstrate that this approach successfully tracks the target phase across the entire $[0, \pi)$ periodicity interval, achieving a measurement precision that surpasses the Standard Quantum Limit and approaches the Heisenberg Limit.

Coherence scanning interferometry (CSI) using a Linnik setup allows surface topography measurements at high numerical aperture (NA), but the dual-objective design demands high alignment precision. Fourier optics-based signal models assuming perfect alignment are well established. In this contribution, we extend such a model to account for axial and lateral misalignment of optical components in the reference arm. Simulated signals show that each misalignment type produces a distinct and identifiable distortion of the interference signal, providing a basis for alignment diagnostics in high-NA CSI systems. Experimental validation is subject of ongoing work.

Freeform optical surfaces are essential for advanced optical systems due to their design flexibility, but accurate reconstruction is challenging because of geometric complexity and inflection regions. This work numerically models a circular freeform surface with a 111 mm diameter, a peak-to-valley (PV) sag of 3.5 mm, an RMS flatness error of 76 nm, and an RMS roughness of 0.5 nm. The surface is represented using XY polynomial functions, with sag data extracted from 180 diametrical profiles via a Profile Rotation Model (PRM) and fitted with eighth-order polynomials. A ray-tracing algorithm simulates one-dimensional fringe intensity distributions at a terahertz wavelength of 0.284 mm. Reconstruction employs a custom Zernike-based fitting routine with 48 terms, enhanced by fringe-thinning, and a radial Zernike strategy that mitigates ambiguities by independently reconstructing azimuthally replicated radial profiles. Accuracy is assessed using shading error analysis and the area between reconstructed and simulated profiles. Results from eight representative profiles show excellent agreement, with an average relative error of 2.29% of the maximum surface height. For larger surfaces, increased sag and slope reduce ray-detection sensitivity, limiting effective measurement to ~174 mm for geometry.

In large infrastructure, small surface defects can propagate and cause structural failures. Regular monitoring of these defects, while hard to conduct manually, is demanded. An automated drone-based laser triangulation system offers ease of regular monitoring and the proximity to achieve the required detection resolution for small surface defects. The developed laser triangulation system achieves an axial resolution of 128 µm on flat polyurethane surfaces, enabling the detection of surface defects with a lateral size down to 5 mm. To ensure operational reliability, the system's performance is evaluated under varying environmental conditions. Furthermore, the impact of drone vibrations and surface properties, such as curvature, surface textures on the measurement results is studied.