Related papers: Kernel Phase and Kernel Amplitude in Fizeau Imagin…
Wavefront sensing involves estimating the phase and intensity of light, enabling a wide range of imaging applications, from adaptive optics and astronomy to biomedical imaging. Since conventional image sensors can only measure the spatial…
Long-baseline interferometry at optical and near-infrared wavelengths is an emerging technology which is quickly becoming a useful tool to investigate stellar atmospheres and to compare observations with models. Stellar atmosphere models…
The well-known phase problem which means image reconstruction from only spectrum magnitude without using any spectrum phase information is considered basically in application to VLBI mapping of compact extragalactic radio sources (active…
Because of atmospheric turbulence, obtaining high angular resolution images with a high dynamic range is difficult even in the near infrared domain of wavelengths. We propose a novel technique to overcome this issue. The fundamental idea is…
Resolving sources beyond the diffraction limit is important in imaging, communications, and metrology. Current image-based methods of super-resolution require phase information (either of the source points or an added filter) and perfect…
We theoretically study the problem of detecting dipole radiation in an optical system of high numerical aperture in which the detector is sensitive to \textit{field amplitude}. In particular, we model the phase sensitive detector as a…
Existing super-resolution methods of optical imaging hold a solid place as an application in natural sciences, but many new developments allow for beating the diffraction limit in a more subtle way. One of the recently explored strategies…
Efficient ion-photon coupling is an important component for large-scale ion-trap quantum computing. We propose that arrays of phase Fresnel lenses (PFLs) are a favorable optical coupling technology to match with multi-zone ion traps. Both…
Intensity interferometry permits very long optical baselines and the observation of sub-milliarcsecond structures. Using planned kilometric arrays of air Cherenkov telescopes at short wavelengths, intensity interferometry may increase the…
Optimal frequency identification in astronomical datasets is crucial for variable star studies, exoplanet detection, and asteroseismology. Traditional period-finding methods often rely on specific parametric assumptions, employ binning…
Phase unwrapping remains a critical and challenging problem in InSAR processing, particularly in scenarios involving complex deformation patterns. In earthquake-related deformation, shallow sources can generate surface-breaking faults and…
The classically defined minimum uncertainty of the optical phase is known as the standard quantum limit or shot-noise limit (SNL) originating in the uncertainty principle of quantum mechanics. Based on SNL, the phase sensitivity is…
Nuclear Magnetic Resonance (NMR) spectroscopy is a crucial analytical technique used for molecular structure elucidation, with applications spanning chemistry, biology, materials science, and medicine. However, the frequency resolution of…
Modern optical spectrographs and optical interferometers push the limits in the spectral and spatial regime, providing important new tools for the exploration of the universe. In this contribution I outline the complementary nature of…
Interferometric imaging is a well established method to image phase objects by mixing the image wavefront with a reference one on a CCD camera. It has also been applied to fast transient phenomena, mostly through the analysis of single…
Phase retrieval in inline holography is a fundamental yet ill-posed inverse problem due to the nonlinear coupling between amplitude and phase in coherent imaging. We present a novel off-the-shelf solution that leverages a diffusion model…
Context: In recent years, stellar intensity interferometry has seen renewed interest from the astronomical community because it can be efficiently applied to Cherenkov telescope arrays. Aims: We have investigated the accuracy that can be…
Random feature approximation is arguably one of the most widely used techniques for kernel methods in large-scale learning algorithms. In this work, we analyze the generalization properties of random feature methods, extending previous…
Measuring and testing dependence between complex objects is of great importance in modern statistics. Most existing work relied on the distance between random variables, which inevitably required the moment conditions to guarantee the…
Medical imaging is key in modern medicine. From magnetic resonance imaging (MRI) to microscopic imaging for blood cell detection, diagnostic medical imaging reveals vital insights into patient health. To predict diseases or provide…