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Treffer: Estimation of the optimal variational parameter via SNR analysis

Title:
Estimation of the optimal variational parameter via SNR analysis
Source:
Scale space and PDE methods in computer vision (Hofgeismar, 7-9 April 2005)Lecture notes in computer science. :230-241
Publisher Information:
Berlin: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 13 ref
Original Material:
INIST-CNRS
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Mathematics, UCLA, Los Angeles, CA 90095, United States
Department of Applied of Mathematics, Tel-Aviv Univ, Tel-Aviv 69978, Israel
Department of Electrical Engineering, Technion, Haifa 32000, Israel
ISSN:
0302-9743
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.16894627
Database:
PASCAL Archive

Weitere Informationen

We examine the problem of finding the optimal weight of the fidelity term in variational denoising. Our aim is to maximize the signal to noise ratio (SNR) of the restored image. A theoretical analysis is carried out and several bounds are established on the performance of the optimal strategy and a widely used method, wherein the variance of the residual part equals the variance of the noise. A necessary condition is set to achieve maximal SNR. We provide a practical method for estimating this condition and show that the results are sufficiently accurate for a large class of images, including piecewise smooth and textured images.