Isotropic divide-stages-to process convolutional neural network enhanced double-ring modulated SPIM microscopy (IDDR-SPIM): achieving 5D super-resolution imaging in live cell

2021 ◽  
Author(s):  
Yuxuan Zhao ◽  
Yao Zhou ◽  
Longbiao Chen ◽  
Peng Wang ◽  
Peng Fei
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Live Cell ◽  
Double Ring ◽  
Resolution Imaging

scholarly journals Convolutional Neural Network Based Models for Improving Super-Resolution Imaging

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 43042-43051 ◽  
Author(s):  
Yingyi Sun ◽  
Wei Zhang ◽  
Hao Gu ◽  
Chao Liu ◽  
Sheng Hong ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Resolution Imaging

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

Instant Live-Cell Super-Resolution Imaging of Cellular Structures by Nanoinjection of Fluorescent Probes

Nano Letters ◽  
2015 ◽  
Vol 15 (2) ◽  
pp. 1374-1381 ◽  
Author(s):  
Simon Hennig ◽  
Sebastian van de Linde ◽  
Martina Lummer ◽  
Matthias Simonis ◽  
Thomas Huser ◽  
...  
Keyword(s):  
Fluorescent Probes ◽  
Super Resolution ◽  
Live Cell ◽  
Cellular Structures ◽  
Resolution Imaging

Live-Cell Super-Resolution Imaging with Synthetic Fluorophores

2012 ◽  
Vol 63 (1) ◽  
pp. 519-540 ◽  
Author(s):  
Sebastian van de Linde ◽  
Mike Heilemann ◽  
Markus Sauer
Keyword(s):  
Super Resolution ◽  
Live Cell ◽  
Resolution Imaging

Learning depth super-resolution by using multi-scale convolutional neural network

2019 ◽  
Vol 36 (2) ◽  
pp. 1773-1783 ◽  
Author(s):  
Masoumeh Zareapoor ◽  
Pourya Shamsolmoali ◽  
Jie Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Multi Scale

scholarly journals Yamatani Activation: Edge Homogeneous Response Super Resolution Neural Network

2020 ◽  
Author(s):  
Takuma Yoshimura
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Dynamic Range ◽  
Super Resolution ◽  
Activation Function

In this research, I propose a two-variable activation function "Yamatani" that satisfies the first-degree homogeneity, and realize a super-resolution convolutional neural network that is independent of the dynamic range and symmetrical about the luminance inversion.

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