Natural images are the consequence of multiple factors related to scene structure, illumination, and imaging.  I will present a multilinear model that computes (nonlinear) manifold representations of image ensembles in which the multiple constituent factors (or modes) are disentangled, analyzed explicitly and their second order statistics encoded. This nonlinear model is computed via a tensor decomposition, known as the N-mode SVD.  I will present TensorFaces, our multilinear representation for automated face recognition, and TensorTextures, a novel, multilinear approach to image-based rendering which is an important problem in computer graphics.  Next, I will present Multilinear Independent Components Analysis (MICA) algorithm which encodes the higher order statistics of different factors associated with image formation. MICA is a generalization of the conventional (linear) independent components analysis (ICA) algorithm.