Composition analysis of white mineral pigment based on convolutional neural network and Raman spectrum

Deep convolutional neural networks for Raman spectrum recognition: a unified solution

The Analyst ◽

10.1039/c7an01371j ◽

2017 ◽

Vol 142 (21) ◽

pp. 4067-4074 ◽

Cited By ~ 81

Author(s):

Jinchao Liu ◽

Margarita Osadchy ◽

Lorna Ashton ◽

Michael Foster ◽

Christopher J. Solomon ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Raman Spectrum ◽

Convolutional Neural Network ◽

Raman Spectra ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks

Classification of unprocessed Raman spectra using a convolutional neural network.

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Feature visualization of Raman spectrum analysis with deep convolutional neural network

Analytica Chimica Acta ◽

10.1016/j.aca.2019.08.064 ◽

2019 ◽

Vol 1087 ◽

pp. 11-19 ◽

Cited By ~ 5

Author(s):

Masashi Fukuhara ◽

Kazuhiko Fujiwara ◽

Yoshihiro Maruyama ◽

Hiroyasu Itoh

Keyword(s):

Neural Network ◽

Raman Spectrum ◽

Convolutional Neural Network ◽

Spectrum Analysis ◽

Deep Convolutional Neural Network ◽

Feature Visualization

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Raman spectrum classification based on transfer learning by a convolutional neural network: application to pesticide detection

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2021.120366 ◽

2021 ◽

pp. 120366

Author(s):

Jiaqi Hu ◽

Yanqiu Zou ◽

Biao Sun ◽

Xinyao Yu ◽

Ziyang Shang ◽

...

Keyword(s):

Neural Network ◽

Raman Spectrum ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Pesticide Detection ◽

Network Application

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CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

10.1055/s-0040-1705059 ◽

2020 ◽

Author(s):

S Kashin ◽

D Zavyalov ◽

A Rusakov ◽

V Khryashchev ◽

A Lebedev

Keyword(s):

Neural Network ◽

Convolutional Neural Network

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No-Reference Utility Estimation with a Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2018.09.iriacv-202 ◽

2018 ◽

Vol 2018 (9) ◽

pp. 202-1-202-6 ◽

Cited By ~ 2

Author(s):

Edward T. Scott ◽

Sheila S. Hemami

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Utility Estimation

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Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-180 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 181-1-181-7

Author(s):

Takahiro Kudo ◽

Takanori Fujisawa ◽

Takuro Yamaguchi ◽

Masaaki Ikehara

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Large Scale ◽

Blind Deconvolution ◽

Training Dataset ◽

Image Deconvolution ◽

Classic Problem ◽

Key Points ◽

Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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