Search Results for author:
Found 15 papers, 3 papers with code
On the use of deep learning for phase recovery
Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing.
Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits
Two scanning operations are required: ptychographic to recover the complex transmissivity of the specimen; and rotation of the specimen to acquire multiple projections covering the 3D spatial frequency domain.
The 3D Structural Phenotype of the Glaucomatous Optic Nerve Head and its Relationship with The Severity of Visual Field Damage
In both approaches, we observed that structural changes were more prominent in the superior and inferior quadrant of the ONH, particularly in the RNFL, the prelamina, and the LC.
Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time
Noninvasive X-ray imaging of nanoscale three-dimensional objects, e. g. integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of complex electromagnetic field through ICs; and tomographic scanning, which collects complex field projections from multiple angles.
Noise-resilient approach for deep tomographic imaging
We propose a noise-resilient deep reconstruction algorithm for X-ray tomography.
AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing
$\mathbf{Conclusions}$: We propose an AI-driven approach that can assess the robustness of a given ONH solely from a single OCT scan of the ONH, and without the need to perform biomechanical testing.
Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)
Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns.
Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis
To this end, we aimed: (1) To compare the performance of two relatively recent geometric deep learning techniques in diagnosing glaucoma from a single OCT scan of the ONH; and (2) To identify the 3D structural features of the ONH that are critical for the diagnosis of glaucoma.
Physics-assisted Generative Adversarial Network for X-Ray Tomography
X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields.
Advantage of Machine Learning over Maximum Likelihood in Limited-Angle Low-Photon X-Ray Tomography
Training the deep generative models with synthetic IC data from CircuitFaker illustrates the capabilities of the learned prior from machine learning.
Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning
Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem with practical implications in medical and biological imaging, manufacturing, automation, and environmental and food security.
On the interplay between physical and content priors in deep learning for computational imaging
That is, the higher the entropy of the training images, the weaker the regularization effect can be imposed.
Learning to Synthesize: Robust Phase Retrieval at Low Photon counts
The quality of inverse problem solutions obtained through deep learning [Barbastathis et al, 2019] is limited by the nature of the priors learned from examples presented during the training phase.
Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery
Deep Neural Network (DNN)-based image reconstruction, despite many successes, often exhibits uneven fidelity between high and low spatial frequency bands.
Lensless computational imaging through deep learning
Deep learning has been proven to yield reliably generalizable answers to numerous classification and decision tasks.
