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University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > Fast Fusion of Multi-band Images: A Powerful Tool for Super-resolution
Fast Fusion of Multi-band Images: A Powerful Tool for Super-resolutionAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Yingzhen Li. Hyperspectral (HS) imaging, which consists of acquiring a same scene in several hundreds of
contiguous spectral bands (a 3D data cube), has opened a new range of relevant applications, such
as target detection [Manolakis and Shaw, 2002], classification [C. In this work, a new multi-band image fusion algorithm to enhance the resolution of HS image has been proposed. By exploiting intrinsic properties of the blurring and down-sampling matrices, a much more efficient fusion method has been developed thanks to a closed-form solution for the Sylvester matrix equation associated with maximizing the likelihood. The main contribution of this fusion scheme is that it gets rid of any simulation-based or optimization-based algorithms which are quite time consuming. Coupled with the alternating direction method of multipliers and the block coordinate descent, the proposed algorithm can be easily generalized to incorporate different priors or hyper-priors for the fusion problem, allowing for Bayesian estimators. This method has been applied to both the fusion of MS and HS images and to the fusion of PAN and HS images. We have tested the proposed algorithm in both synthetic data and real data. Results show that the proposed algorithm compares competitively with existing algorithms with the advantage of reducing the computational complexity significantly. This talk is part of the Machine Learning Reading Group @ CUED series. This talk is included in these lists:
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