Comparison of blind deconvolution- and Patlak analysis-based methods for determining vascular permeability

2021 ◽  
Vol 133 ◽  
pp. 104102
Author(s):  
Joe Tien ◽  
Xuanyue Li ◽  
Raleigh M. Linville ◽  
Evan J. Feldman
Keyword(s):  
Vascular Permeability ◽  
Blind Deconvolution ◽  
Patlak Analysis

Aspects of increased vascular permeability following the intradermal injection of histamine in the rat

Pathology ◽  
1971 ◽  
Vol 3 (3) ◽  
pp. 191-202 ◽  
Author(s):  
Arlie McQueen ◽  
J.V. Hurley
Keyword(s):  
Vascular Permeability ◽  
Intradermal Injection

Blind deconvolution of coloured signals based on higher-order cepstra and data fusion

1993 ◽  
Vol 140 (6) ◽  
pp. 356 ◽  
Author(s):  
A.P. Petropulu ◽  
C.L. Nikias
Keyword(s):  
Data Fusion ◽  
Blind Deconvolution ◽  
Higher Order

Sorbinil prevents diabetes-induced increases in vascular permeability but does not alter collagen cross-linking

Diabetes ◽  
1985 ◽  
Vol 34 (7) ◽  
pp. 703-705 ◽  
Author(s):  
J. R. Williamson ◽  
K. Chang ◽  
E. Rowold ◽  
J. Marvel ◽  
M. Tomlinson ◽  
...  
Keyword(s):  
Vascular Permeability ◽  
Cross Linking ◽  
Collagen Cross Linking

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

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.

Aspects of Increased Vascular Permeability Following the Intradermal Injection of Histamine in the Rat

Pathology ◽  
1971 ◽  
Vol 3 (3) ◽  
pp. 191-202 ◽  
Author(s):  
Arlie McQueen ◽  
J. V. Hurley
Keyword(s):  
Vascular Permeability ◽  
Intradermal Injection

Plasma Volume and Vascular Permeability during Hypoxia and Carbon Monoxide Exposure

1968 ◽  
Vol 21 ◽  
pp. 39-48 ◽  
Author(s):  
J. Siggaard-Andersen ◽  
F. Bonde Petersen ◽  
Thorsten Hansen ◽  
K. Mellemgaard
Keyword(s):  
Carbon Monoxide ◽  
Vascular Permeability ◽  
Plasma Volume
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