Results 21 to 30 of about 412,052 (160)

Reconstructing historical climate fields with deep learning [PDF]

Science Advances
Historical records of climate fields are often sparse because of missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records.
Nils Bochow, Anna Poltronieri, Martin Rypdal, Niklas Boers   +3 more
openaire   +3 more sources

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors [PDF]

, 2020
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy ...
An, Sitong, Aurisano, Adam, Calafiura, Paolo, Cerati, Giuseppe, Farrell, Steven, Gray, Lindsey, Hewes, Jeremy, Ju, Xiangyang, Klijnsma, Thomas, Kowalkowski, Jim, Murnane, Daniel, Pata, Joosep, Pedro, Kevin, Perdue, Gabriel, Prabhat, Spentzouris, Panagiotis, Spiropulu, Maria, Terao, Kazuhiro, Tran, Nhan, Tsaris, Aristeidis, Usher, Tracy, Vlimant, Jean-Roch, Zlokapa, Alexander   +22 more
core   +4 more sources

Deep Learning-Based Automated Magnetic Resonance Image Segmentation of the Lumbar Structure and Its Adjacent Structures at the L4/5 Level

Bioengineering, 2023
(1) Background: This study aims to develop a deep learning model based on a 3D Deeplab V3+ network to automatically segment multiple structures from magnetic resonance (MR) images at the L4/5 level.
Min Wang, Zhihai Su, Zheng Liu, Tao Chen, Zhifei Cui, Shaolin Li, Shumao Pang, Hai Lu   +7 more
doaj   +1 more source

Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning

IEEE Transactions on Medical Imaging, 2021
One primary technical challenge in photoacoustic microscopy (PAM) is the necessary compromise between spatial resolution and imaging speed. In this study, we propose a novel application of deep learning principles to reconstruct undersampled PAM images and transcend the trade-off between spatial resolution and imaging speed.
Anthony DiSpirito, Daiwei Li, Tri Vu, Maomao Chen, Dong Zhang, Jianwen Luo, Roarke Horstmeyer, Junjie Yao   +7 more
openaire   +4 more sources

Compressive Light Field Reconstructions Using Deep Learning [PDF]

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017
Published at CCD 2017 workshop held in conjunction with CVPR ...
Gupta, Mayank, Jauhari, Arjun, Kulkarni, Kuldeep, Jayasuriya, Suren, Molnar, Alyosha, Turaga, Pavan   +5 more
openaire   +2 more sources

DEEP LEARNING FOR 3D BUILDING RECONSTRUCTION: A REVIEW

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022
Abstract. 3D building reconstruction using Earth Observation (EO) data (aerial and satellite imagery, point clouds, etc.) is an important and active research topic in different fields, such as photogrammetry, remote sensing, computer vision and Geographic Information Systems (GIS).
M. Buyukdemircioglu, M. Buyukdemircioglu, M. Buyukdemircioglu, S. Kocaman, S. Kocaman, M. Kada   +5 more
openaire   +5 more sources

A review on deep learning MRI reconstruction without fully sampled k-space

BMC Medical Imaging, 2021
Background Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method in clinical medicine, but it has always suffered from the problem of long acquisition time.
Gushan Zeng, Yi Guo, Jiaying Zhan, Zi Wang, Zongying Lai, Xiaofeng Du, Xiaobo Qu, Di Guo   +7 more
doaj   +1 more source

Reconstructing the kinematics of deep inelastic scattering with deep learning

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2022
We introduce a method to reconstruct the kinematics of neutral-current deep inelastic scattering (DIS) using a deep neural network (DNN). Unlike traditional methods, it exploits the full kinematic information of both the scattered electron and the hadronic-final state, and it accounts for QED radiation by identifying events with radiated photons and ...
Miguel Arratia, Daniel Britzger, Owen Long, Benjamin Nachman   +3 more
openaire   +5 more sources

Event Reconstruction with Deep Learning [PDF]

Proceedings of 38th International Conference on High Energy Physics — PoS(ICHEP2016), 2017
The recent Deep Learning (DL) renaissance has yielded impressive feats in industry and science that illustrate the transformative potential of replacing laborious feature engineering with automatic feature learning to simplify, enhance, and accelerate raw data processing. This document overviews current attempts to apply Deep Learning to
openaire   +1 more source

Image Compressed Sensing Attention Neural Network Based on Residual Feature Aggregation [PDF]

Jisuanji kexue, 2023
Deep learning-based image compressive sensing has received extensive attention due to its powerful learning ability and fast processing speed.With the increase in the depth of convolutional neural networks,the existing image reconstruction methods using ...
WANG Zhenbiao, QIN Yali, WANG Rongfang, ZHENG Huan
doaj   +1 more source