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

Authors:
Artur Loza;David Bull;Nishan Canagarajah;Alin Achim
Affiliations:
Department of Electrical and Electronic Engineering, University of Bristol, Bristol BS8 1UB, UK;Department of Electrical and Electronic Engineering, University of Bristol, Bristol BS8 1UB, UK;Department of Electrical and Electronic Engineering, University of Bristol, Bristol BS8 1UB, UK;Department of Electrical and Electronic Engineering, University of Bristol, Bristol BS8 1UB, UK
Venue:
Computer Vision and Image Understanding
Year:
2010

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Abstract

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.