Results 21 to 30 of about 8,062,965 (379)

Deep learning-based video stream reconstruction in mass-production diffractive optical systems [PDF]

Компьютерная оптика, 2021
Many recent studies have focused on developing image reconstruction algorithms in optical systems based on flat optics. These studies demonstrate the feasibility of applying a combination of flat optics and the reconstruction algorithms in real vision ...
V. Evdokimova, M. Petrov, M. Klyueva, E. Zybin, V. Kosianchuk, I. Mishchenko, V. Novikov, N. Selvesiuk, E. Ershov, N. Ivliev, R. Skidanov, N. Kazanskiy, A. Nikonorov   +12 more
doaj   +1 more source

A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction [PDF]

IEEE Transactions on Medical Imaging, 2017
Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the ...
Jo Schlemper, Jose Caballero, J. Hajnal, A. Price, D. Rueckert   +4 more
semanticscholar   +1 more source

Lightweight Image Super-Resolution Reconstruction Based on Depthwise Separable Convolution [PDF]

Jisuanji gongcheng, 2022
Image super-resolution reconstruction aims to reconstruct a high-resolution image close to the real image according to the low-resolution image.The existing image super-resolution reconstruction methods based on the Convolutional Neural Network(CNN ...
LIU Cong, QU Dan, SI Nianwen, WEI Ziwei
doaj   +1 more source

Generalized Image Reconstruction in Optical Coherence Tomography Using Redundant and Non-Uniformly-Spaced Samples

Sensors, 2021
In this paper, we use Frame Theory to develop a generalized OCT image reconstruction method using redundant and non-uniformly spaced frequency domain samples that includes using non-redundant and uniformly spaced samples as special cases. We also correct
Karim Nagib, Biniyam Mezgebo, Namal Fernando, Behzad Kordi, Sherif S. Sherif   +4 more
doaj   +1 more source

Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction

Scientific Reports, 2021
Recent years have seen a resurgence of interest in inexpensive low magnetic field (
Neha Koonjoo, Bo Zhu, G. Bagnall, D. Bhutto, Matthew S. Rosen   +4 more
semanticscholar   +1 more source

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge [PDF]

Magnetic Resonance in Medicine, 2020
To advance research in the field of machine learning for MR image reconstruction with an open challenge.
F. Knoll, Tullie Murrell, Anuroop Sriram, N. Yakubova, Jure Zbontar, Michael G. Rabbat, Aaron Defazio, Matthew Muckley, D. Sodickson, C. L. Zitnick, M. Recht   +10 more
semanticscholar   +1 more source

Deep Learning-Based Image Reconstruction for Different Medical Imaging Modalities

Computational and Mathematical Methods in Medicine, 2022
Image reconstruction in magnetic resonance imaging (MRI) and computed tomography (CT) is a mathematical process that generates images at many different angles around the patient. Image reconstruction has a fundamental impact on image quality.
Muhammad Yaqub, Jinchao Feng, Kaleem Arshid, Shahzad Ahmed, Wenqian Zhang, Muhammad Zubair Nawaz, Tariq Mahmood   +6 more
semanticscholar   +1 more source

Metal Artifact Reduction in CT: Where Are We After Four Decades?

IEEE Access, 2016
Methods to overcome metal artifacts in computed tomography (CT) images have been researched and developed for nearly 40 years. When X-rays pass through a metal object, depending on its size and density, different physical effects will negatively affect ...
Lars Gjesteby, Bruno De Man, Yannan Jin, Harald Paganetti, Joost Verburg, Drosoula Giantsoudi, Ge Wang   +6 more
doaj   +1 more source

fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning.

Radiology: Artificial Intelligence, 2020
A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented.
F. Knoll, Jure Zbontar, Anuroop Sriram, Matthew Muckley, M. Bruno, Aaron Defazio, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, H. Chandarana, Zizhao Zhang, Michal Drozdzalv, Adriana Romero, Michael G. Rabbat, Pascal Vincent, James Pinkerton, Duo Wang, N. Yakubova, Erich Owens, C. L. Zitnick, M. Recht, D. Sodickson, Y. Lui   +22 more
semanticscholar   +1 more source

On instabilities of deep learning in image reconstruction and the potential costs of AI [PDF]

Proceedings of the National Academy of Sciences of the United States of America, 2019
Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field.
Vegard Antun, F. Renna, C. Poon, B. Adcock, A. Hansen   +4 more
semanticscholar   +1 more source