Spectral blind deconvolution with differential entropy regularization for infrared spectrum

ABSTRACT A procedure for the quantitative determination of 5β-pregnan-3α-ol-20-one in urine is described. After acid hydrolysis of the pregnanolone-conjugates in urine, the free steroids are extracted with toluene. Pregnanolone is isolated in a pure form as its acetate; after chromatographic separation of the free steroids on alumina, the fraction containing pregnanolone is acetylated and rechromatographed on alumina. Quantitative determination of the isolated pregnanolone-acetate is carried out with the aid of the infrared spectrum recorded by a micro KBr-wafermethod. The reliability of the method under various conditions is discussed under the headings, specificity, accuracy, precision and sensitivity. It is possible to determine 30–40 μg pregnanolone in a 24-hours urine portion with a precision of 25%.

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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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Spectral blind deconvolution with differential entropy regularization for infrared spectrum

Blind deconvolution using the similarity of multiscales regularization for infrared spectrum

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INFRARED SPECTRUM OF (Z)-3-IODO-BUT-2-EN-1-YL [ï¿½ CH2CHC(CH3)I] PRODUCED UPON PHOTODISSOCIATION OF (Z)-1,3-DIIODO-BUT-2-ENE [(CH2I) HC=C(CH3)I] IN SOLID PARA-HYDROGEN

DETERMINATION OF MICRO-AMOUNTS OF 5β-PREGNAN-3α-OL-20-ONE IN URINE USING INFRARED-SPECTROMETRY

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

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