Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture [PDF]
ITM Web of Conferences, 2020 Single image super-resolution using deep learning techniques has shown very high reconstruction performance over the last few years. We propose a novel three-dimensional convolutional neural network called 3D FSRCNN based on FSRCNN, which reinstates the ...
Mane Vanita, Jadhav Suchit, Lal Praneya
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Crack‐Growing Interlayer Design for Deep Crack Propagation and Ultrahigh Sensitivity Strain Sensing
Advanced Functional Materials, EarlyView.A crack‐growing semi‐cured polyimide interlayer enabling deep cracks for ultrahigh sensitivity in low‐strain regimes is presented. The sensor achieves a gauge factor of 100 000 at 2% strain and detects subtle deformations such as nasal breathing, highlighting potential for minimally obstructive biomedical and micromechanical sensing applications ...
Minho Kim +11 more
wiley +1 more source
SurfNet: Generating 3D shape surfaces using deep residual networks
, 2017 3D shape models are naturally parameterized using vertices and faces, \ie, composed of polygons forming a surface. However, current 3D learning paradigms for predictive and generative tasks using convolutional neural networks focus on a voxelized ...
Huang, Qixing +3 more
core +1 more source
Image Level Training and Prediction: Intracranial Hemorrhage Identification in 3D Non-Contrast CT
IEEE Access, 2019 Current hardware restrictions pose limitations on the use of convolutional neural networks for medical image analysis. There is a large trade-off between network architecture and input image size.
Ajay Patel +4 more
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Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network
Photonics, 2021 Accurate three-dimensional (3D) shape reconstruction of objects from a single image is a challenging task, yet it is highly demanded by numerous applications.
Hieu Nguyen, Zhaoyang Wang
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Sketch-based 3D shape retrieval using Convolutional Neural Networks [PDF]
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 CVPR ...
Wang, Fang, Kang, Le, Li, Yi
openaire +2 more sources
Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics
Advanced Functional Materials, EarlyView.Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha +18 more
wiley +1 more source
Isointense infant brain MRI segmentation with a dilated convolutional neural network [PDF]
, 2017 Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D ...
Moeskops, Pim, Pluim, Josien P. W.
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Study on a Poisson's Equation Solver Based On Deep Learning Technique
, 2017 In this work, we investigated the feasibility of applying deep learning techniques to solve Poisson's equation. A deep convolutional neural network is set up to predict the distribution of electric potential in 2D or 3D cases.
Dang, Xunwang +6 more
core +1 more source
PLoS Computational Biology, 2018 Three-dimensional microscopy is increasingly prevalent in biology due to the development of techniques such as multiphoton, spinning disk confocal, and light sheet fluorescence microscopies.
Edouard A Hay, Raghuveer Parthasarathy
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