Image De-Quantization Using Plate Bending Model

Algorithms. 2018;11(8):110 DOI 10.3390/a11080110

 

Journal Homepage

Journal Title: Algorithms

ISSN: 1999-4893 (Online)

Publisher: MDPI AG

LCC Subject Category: Technology: Technology (General): Industrial engineering. Management engineering | Science: Mathematics: Instruments and machines: Electronic computers. Computer science

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML, XML

 

AUTHORS

David Völgyes (Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjøvik, Norway)
Anne Catrine Trægde Martinsen (Department of Physics, University of Oslo, 0316 Oslo, Norway)
Arne Stray-Pedersen (Department of Forensic Sciences, Oslo University Hospital, 0424 Oslo, Norway)
Dag Waaler (Department of Health Sciences in Gjøvik, Norwegian University of Science and Technology, 2803 Gjøvik, Norway)
Marius Pedersen (Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjøvik, Norway)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks

 

Abstract | Full Text

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.