Name
Abstract | ||
|---|---|---|
In this paper, we consider hyperspectral unmixing problems where the observed images are blurred during the acquisition process, e.g., in microscopy and spectroscopy. We derive a joint observation and mixing model and show how it affects endmember identifiability within the geometrical unmixing framework. An analysis of the model reveals that nonnegative blurring results in a contraction of both the minimum-volume enclosing and maximum-volume enclosed simplex. We demonstrate this contraction property in the case of a spectrally invariant point-spread function. The benefit of prior deconvolution on the accuracy of the restored sources and abundances is illustrated using simulated and real Raman spectroscopic data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TIP.2014.2300822 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
deconvolution,hyperspectral imaging,noise,vectors,image restoration,raman spectroscopy,mathematical model,indexes,trajectory,point spread function | Computer vision,Deblurring,Pattern recognition,Identifiability,Deconvolution,Hyperspectral imaging,Simplex,Artificial intelligence,Invariant (mathematics),Image restoration,Mathematics,Joint observation | Journal |
Volume | Issue | ISSN |
23 | 3 | 1941-0042 |
Citations | PageRank | References |
5 | 0.46 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Henrot | 1 | 70 | 4.62 |
| Charles Soussen | 2 | 113 | 15.21 |
| Manuel Dossot | 3 | 5 | 0.46 |
| David Brie | 4 | 130 | 24.28 |