The task of enhancing the perception of a scene by combining information captured by different sensors is usually known as image fusion. The pyramid decomposition and the Dual-Tree Wavelet Transform have been thoroughly applied in image fusion as analysis and synthesis tools. Using a number of pixel-based and region-based fusion rules, one can combine the important features of the input images in the transform domain to compose an enhanced image. In this paper, the authors test the efficiency of a transform constructed using Independent Component Analysis (ICA) and Topographic Independent Component Analysis bases in image fusion. The bases are obtained by offline training with images of similar context to the observed scene. The images are fused in the transform domain using novel pixelbased or region-based rules. The proposed schemes feature improved performance compared to traditional wavelet approaches with slightly increased computational complexity.
In a method analogous to this, a feature-based image fusion using independent component analysis (ICA) and topographic independent component analysis bases was introduced REF .
Pixel-based and region-based image fusion schemes using ICA bases
Inf. Fusion
Computer Science