University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Blind separation of image sources via adaptive dictionary learning

Abolghasemi, V, Abolghasemi, V, Ferdowsi, S and Sanei, S (2012) Blind separation of image sources via adaptive dictionary learning IEEE Transactions on Image Processing, 21 (6). pp. 2921-2930.

[img] Text
Blind Separation of Image Sources via Adaptive Dictionary Learning.pdf
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (4MB)
[img] Text (licence)
SRI_deposit_agreement.pdf
Restricted to Repository staff only

Download (33kB)

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. © 2012 IEEE.

Item Type: Article
Authors :
NameEmailORCID
Abolghasemi, VUNSPECIFIEDUNSPECIFIED
Abolghasemi, VUNSPECIFIEDUNSPECIFIED
Ferdowsi, SUNSPECIFIEDUNSPECIFIED
Sanei, SUNSPECIFIEDUNSPECIFIED
Date : June 2012
Identification Number : 10.1109/TIP.2012.2187530
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:43
Last Modified : 31 Oct 2017 14:37
URI: http://epubs.surrey.ac.uk/id/eprint/591157

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800