Non-Gaussian model-based fusion of noisy images in the wavelet domain

AT Loza, DR Bull, N Canagarajah, AM Achim

Research output: Contribution to journalArticle (Academic Journal)peer-review

74 Citations (Scopus)


This paper describes a new methodology for multimodal image fusion based on non-Gaussian statistical modelling of wavelet coefficients. Special emphasis is placed on the fusion of noisy images. The use of families of generalised Gaussian and alpha-stable distributions for modelling image wavelet coefficients is investigated and methods for estimating distribution parameters are proposed. Improved techniques for image fusion are developed, by incorporating these models into a weighted average image fusion algorithm. The proposed method has been shown to perform very well with both noisy and noise-free images from multimodal datasets, outperforming conventional methods in terms of fusion quality and noise reduction in the fused output.
Translated title of the contributionNon-Gaussian model-based fusion of noisy images in the wavelet domain
Original languageEnglish
Pages (from-to)54 - 65
Number of pages12
JournalComputer Vision and Image Understanding
Volume114 (1)
Publication statusPublished - Jan 2010

Bibliographical note

Publisher: Academic Press Inc


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