Image fusion schemes using ICA bases

Image fusion is commonly described as the task of enhancing the perception of a scene by combining information captured by different modality sensors. The pyramid decomposition and the Dual-Tree Wavelet Transform have been employed as analysis and synthesis tools for image fusion by the fusion community. Using various fusion rules, one can combine the important features of the input images in the transform domain to compose an enhanced image. In this study, the authors demonstrate the efficiency of a transform constructed using Independent Component Analysis (ICA) and Topographic Independent Component Analysis bases for image fusion. The bases are trained offline using images of similar context to the observed scene. The images are fused in the transform domain using novel pixel-based or region-based rules. An unsupervised adaptation ICA-based fusion scheme is also introduced. The proposed schemes feature improved performance compared to approaches based on the wavelet transform and slightly increased computational complexity.