High-resolution interior tomography with a deep neural network trained on a low-resolution dataset

2021 ◽  
Author(s):  
Mengzhou Li ◽  
Wenxiang Cong ◽  
Ge Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Deep Neural Network ◽  
Low Resolution ◽  
Resolution Dataset

Learning deconvolutional deep neural network for high resolution medical image reconstruction

2018 ◽  
Vol 468 ◽  
pp. 142-154 ◽  
Author(s):  
Hui Liu ◽  
Jun Xu ◽  
Yan Wu ◽  
Qiang Guo ◽  
Bulat Ibragimov ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Image Reconstruction ◽  
Medical Image ◽  
Deep Neural Network ◽  
Medical Image Reconstruction

scholarly journals The Effect of Visitors on Zoo Reptile Behaviour during the COVID-19 Pandemic

2021 ◽  
Vol 2 (4) ◽  
pp. 664-676
Author(s):  
Kimberley C. Carter ◽  
Isabel A. T. Keane ◽  
Lisa M. Clifforde ◽  
Lewis J. Rowden ◽  
Léa Fieschi-Méric ◽  
...  
Keyword(s):  
High Resolution ◽  
Species Selection ◽  
Low Resolution ◽  
Gekko Gecko ◽  
Video Footage ◽  
Reptile Species ◽  
Zoo Animals ◽  
Species Specific ◽  
The Uk ◽  
Resolution Dataset

Visitors to zoos can have positive, neutral, or negative relationships with zoo animals. This makes human–animal interactions (HAIs) an essential component of welfare and an important consideration in species selection for zoo exhibits and in enclosure designs. We measured the effect of visitors on reptiles by comparing open and closed periods during the lockdowns in response to the COVID-19 pandemic in the UK in a low-resolution dataset for thirteen species of reptiles and a high-resolution dataset focussing on just one of these. Scan sampling on thirteen reptile species (two chelonians and eleven squamates) showed species-specific differences in response to the presence/absence of visitors, with most taxa being only weakly affected. High-resolution scan sampling via video footage of an off-show and on-show enclosure was carried out for tokay geckos (Gekko gecko) over the open and closed periods. In this part of the study, tokay geckos were significantly more visible during zoo closure than when visitors were present on-exhibit, but there was no change in off-show animals, indicating the effect of visitors as opposed to other factors, such as seasonality, which applied equally to both on- and off-show animals. The high-resolution study showed that a significant effect was present for tokay geckos, even though the low-resolution suggested that they were more weakly affected than other taxa. Our results indicate that, for cryptic species such as this, more intensive sampling may be required to properly understand visitor effects. Our data do not allow the interpretation of effects on welfare but show that such assessments require a species-specific approach.

scholarly journals CONVOLUTIONAL NEURAL NETWORK BASED DEM SUPER RESOLUTION

2016 ◽  
Vol XLI-B3 ◽  
pp. 247-250 ◽  
Author(s):  
Zixuan Chen ◽  
Xuewen Wang ◽  
Zekai Xu ◽  
Wenguang Hou
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Low Resolution ◽  
Practical Applications ◽  
Interpolation Methods ◽  
New Strategy

DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.

scholarly journals Super-resolution reconstruction of seismic section image via multi-scale convolution neural network

2021 ◽  
Vol 303 ◽  
pp. 01058
Author(s):  
Meng-Di Deng ◽  
Rui-Sheng Jia ◽  
Hong-Mei Sun ◽  
Xing-Li Zhang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Image Features ◽  
Image Feature ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Seismic Section ◽  
Multi Scale

The resolution of seismic section images can directly affect the subsequent interpretation of seismic data. In order to improve the spatial resolution of low-resolution seismic section images, a super-resolution reconstruction method based on multi-scale convolution is proposed. This method designs a multi-scale convolutional neural network to learn high-low resolution image feature pairs, and realizes mapping learning from low-resolution seismic section images to high-resolution seismic section images. This multi-scale convolutional neural network model consists of four convolutional layers and a sub-pixel convolutional layer. Convolution operations are used to learn abundant seismic section image features, and sub-pixel convolution layer is used to reconstruct high-resolution seismic section image. The experimental results show that the proposed method is superior to the comparison method in peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the total training time and reconstruction time, our method is about 22% less than the FSRCNN method and about 18% less than the ESPCN method.

scholarly journals Rate-Based Decoding of Images from Multi-Region, High Resolution, Electrode Recordings In the Mouse Visual System

2021 ◽  
Author(s):  
Christopher B Fritz
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Neural Activity ◽  
Deep Neural Network ◽  
Superior Performance ◽  
Brain Machine Interface ◽  
Feed Forward ◽  
Deep Networks ◽  
Feed Forward Neural Networks ◽  
Machine Interface

We hypothesize that deep networks are superior to linear decoders at recovering visual stimuli from neural activity. Using high-resolution, multielectrode Neuropixels recordings, we verify this is the case for a simple feed-forward deep neural network having just 7 layers. These results suggest that these feed-forward neural networks and perhaps more complex deep architectures will give superior performance in a visual brain-machine interface.

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