6533b821fe1ef96bd127b626
RESEARCH PRODUCT
A Variational Approach for Denoising Hyperspectral Images Corrupted by Poisson Distributed Noise
Jon Yngve HardebergYvon VoisinMarius PedersenAlamin MansouriFerdinand DegerFerdinand Degersubject
Computational complexity theorybusiness.industryNoise reductionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONHyperspectral imagingPoisson distributionTerm (time)symbols.namesakeNoiseComputer Science::Computer Vision and Pattern RecognitionsymbolsComputer visionArtificial intelligenceMonochromatic colorCubebusinessAlgorithmMathematicsdescription
Poisson distributed noise, such as photon noise is an important noise source in multi- and hyperspectral images. We propose a variational based denoising approach, that accounts the vectorial structure of a spectral image cube, as well as the poisson distributed noise. For this aim, we extend an approach for monochromatic images, by a regularisation term, that is spectrally and spatially adaptive and preserves edges. In order to take the high computational complexity into account, we derive a Split Bregman optimisation for the proposed model. The results show the advantages of the proposed approach compared to a marginal approach on synthetic and real data.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-01-01 |