Constrained image restoration with a multinomial prior

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationProceedings of the International Conference on Image Processing, 1997
Pages259-262
Number of pages4
Volume1
DOIs
Publication statusPublished - 1997
Event4th IEEE International Conference on Image Processing 1997 - Santa Barbara, CA, United States
Duration: 26 Oct 199729 Oct 1997

Conference

Conference4th IEEE International Conference on Image Processing 1997
Abbreviated titleICIP 1997
CountryUnited States
CitySanta Barbara, CA
Period26/10/9729/10/97

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    Calder, B. R., Linnett, L. M., & Carmichael, D. R. (1997). Constrained image restoration with a multinomial prior. In Proceedings of the International Conference on Image Processing, 1997 (Vol. 1, pp. 259-262) https://doi.org/10.1109/ICIP.1997.647754