ABSTRACT
Independent Component Analysis (ICA) is the decomposition technique of a random vector of data into linear components which
are “independent as possible.” Involves finding a suitable representation of multivariate data for computational and conceptual
simplicity, the representation is often sought as a linear transformation of the original data. The linear transformation methods
include Principal Component Analysis (PCA), Factor Analysis, and Projection Pursuit. Here attempt to transmit similar
dimension multiple images as a single linear transformed image using Independent Component Analysis (ICA), Gaussian noise is
added into linearly transformed image. We try to retrieve the original images one by one from noisy transformed image. The
analysis is made by varying noise variance against peak signal to noise ratio (PSNR) with the original image. Our demonstrated
work is highly useful in reducing bandwidth over the channel.
Keywords: Gaussian Noise, Independent Component Analysis (ICA), Principal Component Analysis (PCA), Peak
Signal to Noise Ration (PSNR), Random Vector.