Temporal ICA based source extraction from dynamic image sequences with visual interference under noise

The focus of this work is the development of an efficient adaptive algorithm for source separation from a noisy image sequence with interferences when the underlying source signals are unknown. The sources to be extracted are the underlying activation signals who are collectively responsible for the intensity fluctuations of the pixels. This technique uses the concept of temporal Independent Component Analysis (tICA) which does not rely on source signal information for its unmixing process. Each pixel of an image is taken as a sensor which has its own intensity fluctuation pattern. Thus the whole image is a collection of two dimensional signal mixtures which can be used to separate both super and sub Gaussian sources through an adaption process. This work demonstrates the viability of utilizing the concept of subspace separation and tICA for removal of visual interferences from dynamic image sequences when extracting underlying source signals. Two methods are proposed: The first of which identifies the interference at the ICA output level while the second method removes the interference at the subspace separation stage. The proposed technique has a potential use in a wide array of applications such as computer vision, surveillance bio-medicine, etc.