Constrained image restoration with a multinomial prior

B. R. Calder; L. M. Linnett; D. R. Carmichael

doi:10.1109/ICIP.1997.647754

Constrained image restoration with a multinomial prior

B. R. Calder, L. M. Linnett, D. R. Carmichael

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A Bayesian image processing model is proposed based on a Markovian Multinomial Prior. The technique has application in texture segmentation where its introduction of spatial context can improve segmentation accuracy by 60%. Other applications include general image restoration where 18 dB SNR improvement is possible. In addition, the computational complexity of the system is low, making it ideal as a component part of other systems. We show quantitative experiments to illustrate the performance of the algorithm, and groundtruth examples are provided to show the effect in practice.

Original language	English
Title of host publication	Proceedings of the International Conference on Image Processing, 1997
Pages	259-262
Number of pages	4
Volume	1
DOIs	https://doi.org/10.1109/ICIP.1997.647754
Publication status	Published - 1997
Event	4th IEEE International Conference on Image Processing 1997 - Santa Barbara, CA, United States Duration: 26 Oct 1997 → 29 Oct 1997

Conference

Conference	4th IEEE International Conference on Image Processing 1997
Abbreviated title	ICIP 1997
Country/Territory	United States
City	Santa Barbara, CA
Period	26/10/97 → 29/10/97

Access to Document

10.1109/ICIP.1997.647754

Cite this

@inproceedings{6421d9b58bc34dcc9da12d467741a336,

title = "Constrained image restoration with a multinomial prior",

abstract = "A Bayesian image processing model is proposed based on a Markovian Multinomial Prior. The technique has application in texture segmentation where its introduction of spatial context can improve segmentation accuracy by 60%. Other applications include general image restoration where 18 dB SNR improvement is possible. In addition, the computational complexity of the system is low, making it ideal as a component part of other systems. We show quantitative experiments to illustrate the performance of the algorithm, and groundtruth examples are provided to show the effect in practice.",

author = "Calder, {B. R.} and Linnett, {L. M.} and Carmichael, {D. R.}",

note = "INSPEC Accession Number: 5888876; 4th IEEE International Conference on Image Processing 1997, ICIP 1997 ; Conference date: 26-10-1997 Through 29-10-1997",

year = "1997",

doi = "10.1109/ICIP.1997.647754",

language = "English",

isbn = "0-8186-8183-7",

volume = "1",

pages = "259--262",

booktitle = "Proceedings of the International Conference on Image Processing, 1997",

}

TY - GEN

T1 - Constrained image restoration with a multinomial prior

AU - Calder, B. R.

AU - Linnett, L. M.

AU - Carmichael, D. R.

N1 - INSPEC Accession Number: 5888876

PY - 1997

Y1 - 1997

N2 - A Bayesian image processing model is proposed based on a Markovian Multinomial Prior. The technique has application in texture segmentation where its introduction of spatial context can improve segmentation accuracy by 60%. Other applications include general image restoration where 18 dB SNR improvement is possible. In addition, the computational complexity of the system is low, making it ideal as a component part of other systems. We show quantitative experiments to illustrate the performance of the algorithm, and groundtruth examples are provided to show the effect in practice.

AB - A Bayesian image processing model is proposed based on a Markovian Multinomial Prior. The technique has application in texture segmentation where its introduction of spatial context can improve segmentation accuracy by 60%. Other applications include general image restoration where 18 dB SNR improvement is possible. In addition, the computational complexity of the system is low, making it ideal as a component part of other systems. We show quantitative experiments to illustrate the performance of the algorithm, and groundtruth examples are provided to show the effect in practice.

UR - http://www.scopus.com/inward/record.url?scp=0031341452&partnerID=8YFLogxK

U2 - 10.1109/ICIP.1997.647754

DO - 10.1109/ICIP.1997.647754

M3 - Conference contribution

SN - 0-8186-8183-7

VL - 1

SP - 259

EP - 262

BT - Proceedings of the International Conference on Image Processing, 1997

T2 - 4th IEEE International Conference on Image Processing 1997

Y2 - 26 October 1997 through 29 October 1997

ER -

Constrained image restoration with a multinomial prior

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Cite this