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

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. Key words: multimodal, image fusion, statistical modelling, denoising

• Type: Journal
• Year: 2010
• Where: CVIU
• Authors: Artur Loza, David R. Bull, Nishan Canagarajah, Alin Achim
• Keywords: CVIU 2010, Image Fusion, Multimodal Image Fusion, Non-Gaussian Statistical Modelling