NeuPh:scalable and generalizable neural phase retrieval with local conditional neural fields

Deep learning has transformed computational imaging,but traditional pixel-based representations limit their ability to capture continuous multiscale object features.Addressing this gap,we introduce a local conditional neural field(LCNF)framework,which leverages a continuous neural representation to provide flexible object representations.LCNF’s unique capabilities are demonstrated in solving the highly ill-posed phase retrieval problem of multiplexed Fourier ptychographic microscopy.Our network,termed neural phase retrieval(NeuPh),enables continuous-domain resolution-enhanced phase reconstruction,offering scalability,robustness,accuracy,and generalizability that outperform existing methods.NeuPh integrates a local conditional neural representation and a coordinate-based training strategy.We show that NeuPh can accurately reconstruct high-resolution phase images from low-resolution intensity measurements.Furthermore,NeuPh consistently applies continuous object priors and effectively eliminates various phase artifacts,demonstrating robustness even when trained on imperfect datasets.Moreover,NeuPh improves accuracy and generalization compared with existing deep learning models.We further investigate a hybrid training strategy combining both experimental and simulated datasets,elucidating the impact of domain shift between experiment and simulation.Our work underscores the potential of the LCNF framework in solving complex large-scale inverse problems,opening up new possibilities for deep-learning-based imaging techniques.

High-speed in vitro intensity diffraction tomography

We demonstrate a label-free,scan-free intensity diffraction tomography technique utilizing annular illumination(aIDT)to rapidly characterize large-volume three-dimensional(3-D)refractive index distributions in vitro.By optimally matching the illumination geometry to the microscope pupil,our technique reduces the data requirement by 60 times to achieve high-speed 10-Hz volume rates.Using eight intensity images,we recover volumes of∼350μm×100μm×20μm,with near diffraction-limited lateral resolution of∼487 nm and axial resolution of∼3.4μm.The attained large volume rate and high-resolution enable 3-D quantitative phase imaging of complex living biological samples across multiple length scales.We demonstrate aIDT’s capabilities on unicellular diatom microalgae,epithelial buccal cell clusters with native bacteria,and live Caenorhabditis elegans specimens.Within these samples,we recover macroscale cellular structures,subcellular organelles,and dynamic micro-organism tissues with minimal motion artifacts.Quantifying such features has significant utility in oncology,immunology,and cellular pathophysiology,where these morphological features are evaluated for changes in the presence of disease,parasites,and new drug treatments.Finally,we simulate the aIDT system to highlight the accuracy and sensitivity of the proposed technique.aIDT shows promise as a powerful high-speed,label-free computational microscopy approach for applications where natural imaging is required to evaluate environmental effects on a sample in real time.