Image De-Quantization Using Plate Bending Model
Discretized image signals might have a lower dynamic range than the display. Because of this, false contours might appear when the image has the same pixel value for a larger region and the distance between pixel levels reaches the noticeable difference threshold. There have been several methods aim...
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Online Access: | http://www.mdpi.com/1999-4893/11/8/110 |
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doaj-47a20e61637146169f6d473ccf9584a62020-11-24T23:58:46ZengMDPI AGAlgorithms1999-48932018-07-0111811010.3390/a11080110a11080110Image De-Quantization Using Plate Bending ModelDavid Völgyes0Anne Catrine Trægde Martinsen1Arne Stray-Pedersen2Dag Waaler3Marius Pedersen4Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjøvik, NorwayDepartment of Physics, University of Oslo, 0316 Oslo, NorwayDepartment of Forensic Sciences, Oslo University Hospital, 0424 Oslo, NorwayDepartment of Health Sciences in Gjøvik, Norwegian University of Science and Technology, 2803 Gjøvik, NorwayDepartment of Computer Science, Norwegian University of Science and Technology, 2815 Gjøvik, NorwayDiscretized image signals might have a lower dynamic range than the display. Because of this, false contours might appear when the image has the same pixel value for a larger region and the distance between pixel levels reaches the noticeable difference threshold. There have been several methods aimed at approximating the high bit depth of the original signal. Our method models a region with a bended plate model, which leads to the biharmonic equation. This method addresses several new aspects: the reconstruction of non-continuous regions when foreground objects split the area into separate regions; the incorporation of confidence about pixel levels, making the model tunable; and the method gives a physics-inspired way to handle local maximal/minimal regions. The solution of the biharmonic equation yields a smooth high-order signal approximation and handles the local maxima/minima problems.http://www.mdpi.com/1999-4893/11/8/110de-quantizationfalse contour removalbit depth enhancementbiharmonic equationpartial differential equations |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
David Völgyes Anne Catrine Trægde Martinsen Arne Stray-Pedersen Dag Waaler Marius Pedersen |
spellingShingle |
David Völgyes Anne Catrine Trægde Martinsen Arne Stray-Pedersen Dag Waaler Marius Pedersen Image De-Quantization Using Plate Bending Model Algorithms de-quantization false contour removal bit depth enhancement biharmonic equation partial differential equations |
author_facet |
David Völgyes Anne Catrine Trægde Martinsen Arne Stray-Pedersen Dag Waaler Marius Pedersen |
author_sort |
David Völgyes |
title |
Image De-Quantization Using Plate Bending Model |
title_short |
Image De-Quantization Using Plate Bending Model |
title_full |
Image De-Quantization Using Plate Bending Model |
title_fullStr |
Image De-Quantization Using Plate Bending Model |
title_full_unstemmed |
Image De-Quantization Using Plate Bending Model |
title_sort |
image de-quantization using plate bending model |
publisher |
MDPI AG |
series |
Algorithms |
issn |
1999-4893 |
publishDate |
2018-07-01 |
description |
Discretized image signals might have a lower dynamic range than the display. Because of this, false contours might appear when the image has the same pixel value for a larger region and the distance between pixel levels reaches the noticeable difference threshold. There have been several methods aimed at approximating the high bit depth of the original signal. Our method models a region with a bended plate model, which leads to the biharmonic equation. This method addresses several new aspects: the reconstruction of non-continuous regions when foreground objects split the area into separate regions; the incorporation of confidence about pixel levels, making the model tunable; and the method gives a physics-inspired way to handle local maximal/minimal regions. The solution of the biharmonic equation yields a smooth high-order signal approximation and handles the local maxima/minima problems. |
topic |
de-quantization false contour removal bit depth enhancement biharmonic equation partial differential equations |
url |
http://www.mdpi.com/1999-4893/11/8/110 |
work_keys_str_mv |
AT davidvolgyes imagedequantizationusingplatebendingmodel AT annecatrinetrægdemartinsen imagedequantizationusingplatebendingmodel AT arnestraypedersen imagedequantizationusingplatebendingmodel AT dagwaaler imagedequantizationusingplatebendingmodel AT mariuspedersen imagedequantizationusingplatebendingmodel |
_version_ |
1725449876703019008 |