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 contribution||Non-Gaussian model-based fusion of noisy images in the wavelet domain|
|Pages (from-to)||54 - 65|
|Number of pages||12|
|Journal||Computer Vision and Image Understanding|
|Publication status||Published - Jan 2010|