Paper Info
Title
Blind separation of image sources via adaptive dictionary learning.
Abstract
Sparsity has been shown to be very useful in source separation of multichannel observations. However, in most cases, the sources of interest are not sparse in their current domain and one needs to sparsify them using a known transform or dictionary. If such a priori about the underlying sparse domain of the sources is not available, then the current algorithms will fail to successfully recover the sources. In this paper, we address this problem and attempt to give a solution via fusing the dictionary learning into the source separation. We first define a cost function based on this idea and propose an extension of the denoising method in the work of Elad and Aharon to minimize it. Due to impracticality of such direct extension, we then propose a feasible approach. In the proposed hierarchical method, a local dictionary is adaptively learned for each source along with separation. This process improves the quality of source separation even in noisy situations. In another part of this paper, we explore the possibility of adding global priors to the proposed method. The results of our experiments are promising and confirm the strength of the proposed approach.
Year
DOI
Venue
2012
10.1109/TIP.2012.2187530
IEEE Transactions on Image Processing
Keywords
Field
DocType
sparsity,noise reduction,estimation,noise,adaptive learning,image processing,learning artificial intelligence,hidden markov models,cost function,hidden markov model,dictionaries,blind source separation,vectors
Noise reduction,K-SVD,Pattern recognition,Computer science,A priori and a posteriori,Image processing,Artificial intelligence,Prior probability,Hidden Markov model,Blind signal separation,Source separation
Journal
Volume
Issue
ISSN
21
6
1941-0042
Citations 
PageRank 
References 
25
1.04
18
Authors
3
Name
Order
Citations
PageRank
Vahid Abolghasemi127422.58
Saideh Ferdowsi214710.85
Saeid Sanei353072.63