Corpus ID: 225103327

Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation

@article{Lawrence2020ExploringGA,
  title={Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation},
  author={Nickey Lizbat Lawrence and Mingren Shen and Ruiqing Yin and Cloris Feng and Dane Morgan},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.15315}
}
The use of accurate scanning transmission electron microscopy (STEM) image simulation methods require large computation times that can make their use infeasible for the simulation of many images. Other simulation methods based on linear imaging models, such as the convolution method, are much faster but are too inaccurate to be used in application. In this paper, we explore deep learning models that attempt to translate a STEM image produced by the convolution method to a prediction of the high… Expand

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SHOWING 1-10 OF 20 REFERENCES
Image-to-Image Translation with Conditional Adversarial Networks
TLDR
Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Expand
A survey on Image Data Augmentation for Deep Learning
TLDR
This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing DataAugmentation, a data-space solution to the problem of limited data. Expand
Fast approximate STEM image simulations from a machine learning model
TLDR
This work develops and validate a method that generates an image from the convolution of an object function and the probe intensity, and then uses a multivariate polynomial fit to a dataset of corresponding multislice and convolution images to correct it. Expand
Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images
TLDR
Targeted feature dropout (TFD) is proposed to enhance the robustness of the model to variations in target images, guided by attention to stochastically remove some of the most discriminative features, making it well-suited for small neuroimaging datasets. Expand
Generative adversarial networks and adversarial methods in biomedical image analysis
TLDR
An introduction to GANs and adversarial methods is provided, with an overview of biomedical image analysis tasks that have benefited from such methods, and potential future research directions are proposed. Expand
NIPS 2016 Tutorial: Generative Adversarial Networks
TLDR
This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs), and describes state-of-the-art image models that combine GANs with other methods. Expand
The GAN Landscape: Losses, Architectures, Regularization, and Normalization
TLDR
This work reproduces the current state of the art of GANs from a practical perspective, discusses common pitfalls and reproducibility issues, and goes beyond fairly exploring the GAN landscape. Expand
A streaming multi-GPU implementation of image simulation algorithms for scanning transmission electron microscopy
TLDR
A potentially important application of Prismatic is demonstrated, using it to compute images for atomic electron tomography at sufficient speeds to include in the reconstruction pipeline. Expand
Image quality assessment: from error visibility to structural similarity
TLDR
A structural similarity index is developed and its promise is demonstrated through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. Expand
Three-dimensional imaging of individual point defects using selective detection angles in annular dark field scanning transmission electron microscopy.
TLDR
It is shown that selecting a small range of low scattering angles can make the contrast of the defect-containing atomic columns substantially more depth-dependent, opening new possibilities for highly precise 3D structural characterization of individual point defects in functional materials. Expand
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