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SUMMARY:Multi-band Image Super-resolution - Dr Qi Wei\, CUED
DTSTART:20160926T140000Z
DTEND:20160926T150000Z
UID:TALK67903@talks.cam.ac.uk
CONTACT:Prof. Ramji Venkataramanan
DESCRIPTION:Multi-band imaging\, which consists of acquiring a same scene 
 in several hundreds of\ncontiguous spectral bands (a 3D data cube)\, has o
 pened a new range of relevant applications\, such\nas target detection [Ma
 nolakis and Shaw\, 2002]\, classification [C.-I Chang\, 2003] and spectral
  unmixing\n[Bioucas-Dias et al.\, 2012]. However\, while multi-band sensor
 s provide abundant spectral information\,\ntheir spatial resolution is gen
 erally more limited. Thus\, fusing the multi-band image with other highly\
 nresolved images of the same scene\, such as multispectral (MS) or panchro
 matic (PAN) images is\nan interesting problem\, also known as multi-resolu
 tion image fusion.\nFrom an application point of view\, this problem is al
 so important as motivated by recent national\nprograms\, e.g.\, the Japane
 se next-generation space-borne hyperspectral image suite (HISUI)\, which\n
 fuses co-registered MS and HS images acquired over the same scene under th
 e same conditions\n[Yokoya and Iwasaki\, 2013]. \n\nIn this talk\, a new m
 ulti-band image fusion algorithm to enhance the resolution of multi-band i
 mage\nhas been proposed. By exploiting intrinsic properties of the blurrin
 g and down-sampling matrices\,\na much more efficient fusion method has be
 en developed thanks to a closed-form solution for the\nSylvester matrix eq
 uation associated with maximizing the likelihood. The main contribution of
  this\nfusion scheme is that it gets rid of any simulation-based or optimi
 zation-based algorithms which\nare quite time consuming. Coupled with the 
 alternating direction method of multipliers and the block\ncoordinate desc
 ent\, the proposed algorithm can be easily generalized to incorporate diff
 erent priors or\nhyper-priors for the fusion problem\, allowing for Bayesi
 an estimators. We have tested the\nproposed algorithm in both synthetic da
 ta and real data. Results show that the proposed algorithm\ncompares compe
 titively with existing algorithms with the advantage of reducing the compu
 tational\ncomplexity significantly.\n
LOCATION:LR6\, Cambridge University Engineering Department
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