Estimation of the optimal variational parameter via SNR analysis

Guy Gilboa; Nir A. Sochen; Yehoshua Y. Zeevi

doi:10.1007/11408031_20

Estimation of the optimal variational parameter via SNR analysis

Guy Gilboa^*, Nir A. Sochen, Yehoshua Y. Zeevi

^*Corresponding author for this work

School of Mathematical Sciences

Research output: Contribution to journal › Conference article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	230-241
Number of pages	12
Journal	Lecture Notes in Computer Science
Volume	3459
DOIs	https://doi.org/10.1007/11408031_20
State	Published - 2005
Event	5th International Conference on Scale Space and PDE Methods in Computer Vision, Scale-Space 2005 - Hofgeismar, Germany Duration: 7 Apr 2005 → 9 Apr 2005

Access to Document

10.1007/11408031_20

Cite this

@article{66fb33bf2f774c128315d523e30dd4db,

title = "Estimation of the optimal variational parameter via SNR analysis",

abstract = "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.",

author = "Guy Gilboa and Sochen, {Nir A.} and Zeevi, {Yehoshua Y.}",

year = "2005",

doi = "10.1007/11408031_20",

language = "אנגלית",

volume = "3459",

pages = "230--241",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Springer Verlag",

note = "5th International Conference on Scale Space and PDE Methods in Computer Vision, Scale-Space 2005 ; Conference date: 07-04-2005 Through 09-04-2005",

}

TY - JOUR

T1 - Estimation of the optimal variational parameter via SNR analysis

AU - Gilboa, Guy

AU - Sochen, Nir A.

AU - Zeevi, Yehoshua Y.

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=24644489271&partnerID=8YFLogxK

U2 - 10.1007/11408031_20

DO - 10.1007/11408031_20

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.conferencearticle???

AN - SCOPUS:24644489271

SN - 0302-9743

VL - 3459

SP - 230

EP - 241

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

T2 - 5th International Conference on Scale Space and PDE Methods in Computer Vision, Scale-Space 2005

Y2 - 7 April 2005 through 9 April 2005

ER -

Estimation of the optimal variational parameter via SNR analysis

Abstract

Access to Document

Other files and links

Fingerprint

Cite this