A Mixing and Separation Method of Signals + Color Images Based on Two-Dimensional CCA

Abstract

Blind Source Separation (BSS) is a traditional and challenging problem in signal processing, in which the mixed signals can be separated according to the independence of source signals. The one-dimensional CCA-based signal and color image mixing and separation method needs to reshape the image into vector data, which destroys the spatial structure of the image and affects the recovery effect of the color image. To this end, a mixing and separation method of signals + color images based on two-dimensional CCA, in this paper, is proposed. This method utilizes the auto-correlation among original color images and signals to recover signals and images with high qualities. Comparative experiments with one-dimensional CCA on the COIL-100 data set show that the proposed method is effective and high-speed.