The complete characterization of spatial coherence is extremely difficult because the mutual coherence function (MCF) is a complex-valued function of four independent Cartesian coordinates. This difficulty limits the ability to control and to optimize the spatial coherence in a broad range of key applications. Here we propose an efficient and robust scheme for measuring the complete MCF of an arbitrary partially coherent beam using self-referencing holography, which does not require any prior knowledge or making any assumptions about the MCF. We further apply our method to lensless diffractive imaging, and experimentally demonstrate the reconstruction of a phase object under spatially partially coherent illumination. This application is particularly useful for imaging at short wavelengths, where the illumination sources lack spatial coherence and no high-quality imaging optics are available.
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http://dx.doi.org/10.1364/OE.26.004479 | DOI Listing |
Sci Total Environ
January 2025
Universidad de Santiago de Chile, Santiago, Chile.
Assessing future snow cover changes is challenging because the high spatial resolution required is typically unavailable from climate models. This study, therefore, proposes an alternative approach to estimating snow changes by developing a super-spatial-resolution downscaling model of snow depth (SD) for Japan using a convolutional neural network (CNN)-based method, and by downscaling an ensemble of models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset. After assessing the coherence of the observed reference SD dataset with independent observations, we leveraged it to train the CNN downscaling model; following its evaluation, we applied the trained model to CMIP6 climate simulations.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Satellite Application Division, Korea Aerospace Research Institute (KARI), Daejeon 34133, Republic of Korea.
For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Chemistry & Environmental Science, Jordan Hu College of Science and Liberal Arts, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
Nanoparticles (NPs) have been successfully used as drug delivery systems. To develop and optimize NP-based drug delivery systems, it is essential to understand the dynamics of cell-NP interactions. Quantitative phase imaging techniques enable label-free imaging and have the potential to reveal how cells interact with NPs.
View Article and Find Full Text PDFJ Pers Med
January 2025
Department of Ophthalmology, Mayo Clinic, Rochester, MN 55905, USA.
: Augmented reality (AR) may allow vitreoretinal surgeons to leverage microscope-integrated digital imaging systems to analyze and highlight key retinal anatomic features in real time, possibly improving safety and precision during surgery. By employing convolutional neural networks (CNNs) for retina vessel segmentation, a retinal coordinate system can be created that allows pre-operative images of capillary non-perfusion or retinal breaks to be digitally aligned and overlayed upon the surgical field in real time. Such technology may be useful in assuring thorough laser treatment of capillary non-perfusion or in using pre-operative optical coherence tomography (OCT) to guide macular surgery when microscope-integrated OCT (MIOCT) is not available.
View Article and Find Full Text PDFBiomimetics (Basel)
January 2025
Group of Biomechatronics, Fachgebiet Biomechatronik, Technische Universität Ilmenau, D-98693 Ilmenau, Germany.
Anguilliform locomotion, an efficient aquatic locomotion mode where the whole body is engaged in fluid-body interaction, contains sophisticated physics. We hypothesized that data-driven modeling techniques may extract models or patterns of the swimmers' dynamics without implicitly measuring the hydrodynamic variables. This work proposes empirical kinematic control and data-driven modeling of a soft swimming robot.
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