Name
Title | ||
|---|---|---|
Blind source separation of images based on general cross correlation of linear operators |
Abstract | ||
|---|---|---|
Blind source separation is a process in which mixed signals, obtained as a linear combination of various source signals, are decomposed into their original sources. The source signals and their mixture weights are unknown, but a priori information about their statistical behavior and mixing model is available. In this paper, a new algorithm based on generalized cross correlation linear-operator set is proposed. This algorithm significantly improves source-separation quality compared to several other well-known algorithms, such as subband decomposition independent component analysis, block Gaussian likelihood, and convex analysis of mixtures of non-negative sources. (C) 2011 SPIE and IS&T. [DOI: 10.1117/1.3596620] |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1117/1.3596620 | JOURNAL OF ELECTRONIC IMAGING |
DocType | Volume | Issue |
Journal | 20 | 2 |
ISSN | Citations | PageRank |
1017-9909 | 1 | 0.37 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noam Shamir | 1 | 17 | 2.45 |
| Zeev Zalevsky | 2 | 28 | 15.70 |
| Leonid P. Yaroslavsky | 3 | 108 | 19.34 |
| Bahram Javidi | 4 | 110 | 20.30 |