A new blind image source separation algorithm based on feedback sparse component analysis

In this paper, a new blind source separation (BSS) algorithm for mixed images, called feedback sparse component analysis (FSCA), is proposed. The algorithm develops the sparse component analysis (SCA) and utilizes feedback mechanism to extract the image sources which are not sufficiently sparse to the SCA method, such as noise or complex images with low sparseness. It is experimentally shown that the proposed method does not need vast iteration and can effectively separate all un-sparse sources from the mixtures. Compared to classic fast independent component analysis (FastICA) algorithm, the presented algorithm has better accuracy.

► A question of the BSS model.
► The anti-noise performance tests of the SCA algorithm.
► BSS algorithm based on feedback SCA (FSCA).