Blind deconvolution in ocean waveguides using artificial time reversal

2004 ◽  
Vol 116 (1) ◽  
pp. 262-271 ◽  
Author(s):  
Karim G. Sabra ◽  
David R. Dowling
Keyword(s):  
Time Reversal ◽  
Blind Deconvolution ◽  
Ocean Waveguides

Blind deconvolution in ocean sound channels via artificial time reversal

2005 ◽  
Vol 118 (3) ◽  
pp. 2028-2028
Author(s):  
David R. Dowling ◽  
Karim G. Sabra ◽  
Mark R. Stevenson
Keyword(s):  
Time Reversal ◽  
Blind Deconvolution

scholarly journals Blind deconvolution of sources of opportunity in ocean waveguides using bilinear channel models

2020 ◽  
Vol 148 (4) ◽  
pp. 2267-2279
Author(s):  
Ning Tian ◽  
Kiryung Lee ◽  
Justin Romberg ◽  
Nicholas Durofchalk ◽  
Karim Sabra
Keyword(s):  
Blind Deconvolution ◽  
Channel Models ◽  
Ocean Waveguides

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.

Time Reversal in Lossy Material: An Assessment

PIERS Online ◽  
2006 ◽  
Vol 3 (8) ◽  
pp. 1259-1263
Author(s):  
Ian Scott ◽  
Ana Vukovic ◽  
Phillip Sewell
Keyword(s):  
Time Reversal ◽  
Lossy Material

Thermoacoustic imaging using time reversal

2007 ◽  
Author(s):  
Hubert Grün ◽  
Christian Hofer ◽  
Markus Haltmeier ◽  
Günther Paltauf ◽  
Peter Burgholzer
Keyword(s):  
Time Reversal ◽  
Thermoacoustic Imaging

Blind Deconvolution to Improve Classification of Transient Source Signals in Multipath

2000 ◽  
Author(s):  
Lisa A. Pflug ◽  
George B. Smith ◽  
Michael K. Broadhead
Keyword(s):  
Blind Deconvolution ◽  
Transient Source ◽  
Source Signals
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