000169246 001__ 169246
000169246 005__ 20190316235218.0
000169246 020__ $$a978-1-4673-0046-9
000169246 02470 $$2ISI$$a000312381402206
000169246 037__ $$aCONF
000169246 245__ $$aHYPERSPECTRAL IMAGE COMPRESSED SENSING VIA LOW-RANK AND JOINT-SPARSE MATRIX RECOVERY
000169246 269__ $$a2012
000169246 260__ $$aNew York$$bIeee$$c2012
000169246 300__ $$a4
000169246 336__ $$aConference Papers
000169246 520__ $$aWe propose a novel approach to reconstruct Hyperspectral images from very few number of noisy compressive measure- ments. Our reconstruction approach is based on a convex minimiza- tion which penalizes both the nuclear norm and the l2,1 mixed-norm of the data matrix. Thus, the solution tends to have a simultane- ously low-rank and joint-sparse structure. We explain how these two assumptions fit the Hyperspectral data, and by severals simulations we show that our proposed reconstruction scheme significantly enhances the state-of-the-art tradeoffs between the reconstruction error and the required number of CS measurements.
000169246 6531_ $$aHyperspectral images
000169246 6531_ $$aCompressed sensing
000169246 6531_ $$aJoint sparse signals
000169246 6531_ $$aLow rank matrix recovery
000169246 6531_ $$aNuclear norm
000169246 6531_ $$aLTS2
000169246 700__ $$0242923$$aGolbabaee, Mohammad$$g171628
000169246 700__ $$0240428$$aVandergheynst, Pierre$$g120906
000169246 7112_ $$aThe 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012)
000169246 773__ $$q2741-2744$$t2012 Ieee International Conference On Acoustics, Speech And Signal Processing (Icassp)
000169246 8564_ $$s657848$$uhttps://infoscience.epfl.ch/record/169246/files/golbabaee_icassp12.pdf$$yn/a$$zn/a
000169246 909C0 $$0252392$$pLTS2$$xU10380
000169246 909CO $$ooai:infoscience.tind.io:169246$$pconf$$pSTI$$qGLOBAL_SET
000169246 917Z8 $$x171628
000169246 917Z8 $$x171628
000169246 917Z8 $$x171628
000169246 917Z8 $$x171628
000169246 917Z8 $$x171628
000169246 917Z8 $$x171628
000169246 917Z8 $$x120906
000169246 937__ $$aEPFL-CONF-169246
000169246 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000169246 980__ $$aCONF