Deep learning coherent diffractive imaging
WebMay 20, 2024 · Color transparency, the reduction of initial-state or final-state interactions in coherent nuclear processes, is a natural prediction of QCD (quantum chromodynamics) provided that small-sized or point-like configurations (PLCs) are formed in high-momentum transfer, high-energy, semi-exclusive processes. I use the Frankfurt-Miller-Strikman … Webresolution electron imaging. Three experiments with different electron microscopes and detectors on different samples presented here demonstrate the universality and potential …
Deep learning coherent diffractive imaging
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WebCoherent diffractive imaging (CDI) is a "lensless" technique for 2D or 3D reconstruction of the image of nanoscale structures such as nanotubes, nanocrystals, porous … WebOct 17, 2024 · An optical diffractive neural network (DNN) can be implemented with a cascaded phase mask architecture. Like an optical computer, the system can perform machine learning tasks such as number digit recognition in an all-optical manner. However, the system can work only under coherent light illumination, and the precision …
WebHe is interested in imaging biological samples at atomic resolution and applying coherent diffractive imaging (CDI) to in situ structures. His past work includes phase retrieval for diffraction patterns obtained with an … WebApr 1, 2024 · 1. Introduction. Deep learning has been received as an amazing paradigm for image recognition, language translation, computational imaging, and so on [1], it is being implemented in both academia and industry with an explosive growth.The applications strongly require ultra-fast speed and low latency hardware that mimicks the biological …
WebJul 30, 2024 · Ptychographic imaging is a powerful means of imaging beyond the resolution limits of typical x-ray optics. Recovering images from raw ptychographic data, however, requires the solution of an inverse problem, namely, phase retrieval. Phase retrieval algorithms are computationally expensive, which precludes real-time imaging. WebSep 9, 2024 · Here, we introduce a physics-driven untrained learning method, termed Deep CDI, which addresses the above problem and can image a dynamic process with high confidence and fast reconstruction ...
WebMar 19, 2024 · Coherent Diffractive Imaging (CDI) is an imaging technique for probing physical structure of materials at molecular and nanoscale. It is a widely used technique in material science, with modern beamline ... deep learning based method to perform phase retrieval for ptychography. Ptychography is a CDI method that captures multiple
WebJun 3, 2024 · @article{osti_1871070, title = {AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging}, author = {Yao, Yudong and Chan, Henry and Sankaranarayanan, Subramanian and Balaprakash, Prasanna and Harder, Ross J. and Cherukara, Mathew J.}, abstractNote = {Abstract The … gui lin salineWebNov 9, 2024 · In situ characterization of detailed 3D views of defects and interfaces and their evolution at the mesoscale (a few nm to hundreds of μ m) are required to develop microstructure-aware physics-based models and to design advanced materials with tailored properties. 1,2 1. D. L. McDowell, “ A perspective on trends in multiscale plasticity,” Int. J. … pillivuyt julestelWebJul 6, 2024 · All-optical machine learning using diffractive deep neural networks. Science 361 , 1004–1008 (2024). Article ADS MathSciNet Google Scholar guilin taijiWebJan 6, 2024 · Abstract. We report the development of deep-learning coherent electron diffractive imaging at subangstrom resolution using convolutional neural networks … pillivuyt kopperWebNov 8, 2024 · Central to many imaging techniques including coherent X-ray diffraction imaging 1,2, electron microscopy 3, astronomy 4 and super-resolution optical imaging 5, is the process of phase retrieval ... pillivuyt kopWebDiffractive imaging of individual nanoparticles creates huge data sets which are necessary for successful structure determination. ... that deep neural networks can be used to classify large amounts of diffraction data … guillain barre johnson and johnson vacunaWebLaboratory has built a tomographic diffractive microscope with an unsurpassed 130nm resolution but a low imaging speed - no less than one minute. Afterwards, a high-end PC reconstructs the 3D image in 20 seconds. We now expect an interactive system providing preview images during the acquisition for monitoring purposes. We first present a pillivuyt kurv