Computational ghost imaging using deep learning
Tomoyoshi Shimobaba,Yutaka Endo,Takashi Nishitsuji,Takayuki Takahashi,Yuki Nagahama,Satoki Hasegawa,Marie Sano,Ryuji Hirayama,Takashi Kakue,Atsushi Shiraki,Tomoyoshi Ito +10 more
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TLDR
A deep neural network is used to automatically learn the features of noise-contaminated CGI images and is able to predict low-noise images from new noise- Contamination CGI images.About:
This article is published in Optics Communications.The article was published on 2018-04-15 and is currently open access. It has received 112 citations till now. The article focuses on the topics: Ghost imaging.read more
Citations
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Journal ArticleDOI
Single-pixel imaging 12 years on: a review
TL;DR: This review considers the development of single-pixel cameras from the seminal work of Duarte et al. up to the present state of the art, covering the variety of hardware configurations, design of mask patterns and the associated reconstruction algorithms, many of which relate to the field of compressed sensing and, more recently, machine learning.
Journal ArticleDOI
Hadamard single-pixel imaging versus Fourier single-pixel imaging.
TL;DR: In this article, the authors compare the performance of HSI and Fourier single-pixel imaging with theoretical analysis and experiments, and show that FSI is more efficient than HSI while HSI was more noise-robust than FSI.
Journal ArticleDOI
Improving the signal-to-noise ratio of single-pixel imaging using digital microscanning
TL;DR: This work applies a digital microscanning approach to an infrared single-pixel camera that improves the SNR of reconstructed images by ∼ 50 % for the same acquisition time and provides access to a stream of low-resolution 'preview' images throughout each high-resolution acquisition.
Journal ArticleDOI
X-ray ghost imaging with a laboratory source.
A. Schori,Sharon Shwartz +1 more
TL;DR: The results advance the possibilities that the high-resolution method of ghost diffraction will be utilized with tabletop X-ray sources, by reconstructing images of 10 and 100 μm slits with very high contrast.
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Is ghost imaging intrinsically more powerful against scattering
TL;DR: Experimental comparison between ghost imaging and traditional non-correlated imaging under disturbance of scattering shows ghost imaging appears more robust than traditional imaging, which will be useful in harsh environment.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal Article
Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.