Abstract

Assessment of Mutual Information Based Least Dependent Component Analysis in Comparison with Fast ICA for Blind Source Separation of Images

Author(s): Parimala Gandhi A and Vijayan S

Blind source separation (BSS) is the process of separating the source signals from the combined observation signals without having any idea of the source signals as well as the method of combination. Though many methods are now emerging, based on performance and quality of separated signals MILCA (mutual information based least dependent component analysis) method has its outstanding capability with better separation quality and signal to noise ratio (SNR). Its main drawback which we have analyzed is that the time taken for separation of sources is very high compared to any other existing methods. It is normally in terms of hours when the number of source signals increases. Particularly when the number of source images goes beyond 9 or 10, it takes even a day for separation of all the images in which case all other methods take time just in terms of hours. One more observation we made is when the number of sources is increased, the performance of MILCA method becomes worse compared to other methods. So, the MILCA method is preferable one who is so picky about the separation quality and SNR. Instead of considering the ordinary images if the medical images like scanned images of any of the human body parts, EEG, ECG, MEG and Photographs of medical images can also be considered and BSS concepts can be applied for retrieving a clear and diagnosable images for further analysis about the diseases.