Open AccessMULTI-CHANNEL IMAGE SOURCE SEPARATION BY DICTIONARY UPDATE METHOD
Author(s) -
D. Sugumar,
Anju Thomas,
P. T. Vanathi
Publication year - 2014
Publication title -
international journal of electronic signal and systems
Language(s) - English
Resource type - Journals
ISSN - 2231-5969
DOI - 10.47893/ijess.2014.1169
Subject(s) - source separation , computer science , blind signal separation , k svd , artificial intelligence , set (abstract data type) , domain (mathematical analysis) , pattern recognition (psychology) , channel (broadcasting) , image (mathematics) , algorithm , mathematics , computer network , mathematical analysis , programming language
In real world, a large set of mixed signals are available from which each source signal need to be recovered and this problem can be addressed with adaptive dictionary method. In the case of multichannel observations sparsity found to be very useful for source separation. The problem exist is that in most cases the sources are not sparsified in their domain and it will become necessary to sparsify the source by using some known dictionaries. In order to recover the sources successfully a prior knowledge of the sparse domain is required, if not available this problem can be solved by using dictionary learning technique into source separation. The proposed method, a local dictionary is adaptively learned for each source separately along with separation. This approach improves the quality of source separation both in noiseless and different noisy situations. The advantage of this method is that it denoise the sources during separation.