scholarly journals Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network

2018 ◽  
Vol 146 ◽  
pp. 305-319 ◽  
Author(s):  
Charis Lanaras ◽  
José Bioucas-Dias ◽  
Silvano Galliani ◽  
Emmanuel Baltsavias ◽  
Konrad Schindler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Super Resolution ◽  
Sentinel 2

scholarly journals Far-field super-resolution ghost imaging with a deep neural network constraint

2022 ◽  
Vol 11 (1) ◽  
Author(s):  
Fei Wang ◽  
Chenglong Wang ◽  
Mingliang Chen ◽  
Wenlin Gong ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Model ◽  
Deep Neural Network ◽  
Super Resolution ◽  
Far Field ◽  
Ghost Imaging ◽  
Practical Applications ◽  
Sampling Ratio ◽  
Network Output ◽  
Single Pixel

AbstractGhost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We experimentally demonstrate the proposed GI technique by imaging a flying drone, and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications.

scholarly journals Deep Neural Network-Based Sinogram Super-Resolution and Bandwidth Enhancement for Limited-Data Photoacoustic Tomography

2020 ◽  
Vol 67 (12) ◽  
pp. 2660-2673 ◽  
Author(s):  
Navchetan Awasthi ◽  
Gaurav Jain ◽  
Sandeep Kumar Kalva ◽  
Manojit Pramanik ◽  
Phaneendra K. Yalavarthy
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Super Resolution ◽  
Photoacoustic Tomography ◽  
Limited Data ◽  
Bandwidth Enhancement

scholarly journals Super resolution DOA estimation based on deep neural network

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Wanli Liu
Keyword(s):  
Neural Network ◽  
Potential Application ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Doa Estimation ◽  
Angular Distance ◽  
Signal To Noise ◽  
Simulation Results

AbstractRecently, deep neural network (DNN) studies on direction-of-arrival (DOA) estimations have attracted more and more attention. This new method gives an alternative way to deal with DOA problem and has successfully shown its potential application. However, these works are often restricted to previously known signal number, same signal-to-noise ratio (SNR) or large intersignal angular distance, which will hinder their generalization in real application. In this paper, we present a novel DNN framework that realizes higher resolution and better generalization to random signal number and SNR. Simulation results outperform that of previous works and reach the state of the art.

scholarly journals M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4765
Author(s):  
Iván de-Paz-Centeno ◽  
María Teresa García-Ordás ◽  
Oscar García-Olalla ◽  
Javier Arenas ◽  
Héctor Alaiz-Moretón
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Super Resolution ◽  
Network Approach ◽  
Neural Network Approach ◽  
Load Profile ◽  
Hourly Data

We propose M-SRPCNN, a fully convolutional generative deep neural network to recover missing historical hourly data from a sensor based on the historic monthly energy consumption. The network performs a reconstruction of the load profile while keeping the overall monthly consumption, which makes it suitable to effectively replace energy apportioning systems. Experiments demonstrate that M-SRPCNN can effectively reconstruct load curves from single month overall values, outperforming traditional apportioning systems.

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