Study of ICA algorithm for separation of mixed images

The image data can be Gaussian or non-Gaussian or both. If the data is Gaussian then the extraction and processing of image data becomes computationally less complex. Due to this reason many existing techniques like factor analysis, Principle Component analysis, Gabor wavelets etc. assume the data to be Gaussian and processing involves only second order moments such as mean and variance. But if the data is non-Gaussian, then the extraction and processing of image data becomes computationally more complex as it involves higher order moments like kurtosis and a new measure of non-Gaussianity known as negentropy. In this paper a recently developed technique, known as Independent Component Analysis, is applied to image data and detailed analysis is done for step wise output of the algorithm. In the context of adaptive Neural Network, ICA method tries to train the non-Gaussianity instead of assuming the data to be Gaussian.