0000000000484954

AUTHOR

Ferdinand Deger

showing 4 related works from this author

A Variational Approach for Denoising Hyperspectral Images Corrupted by Poisson Distributed Noise

2014

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.

Computational complexity theorybusiness.industryNoise reductionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONHyperspectral imagingPoisson distributionTerm (time)symbols.namesakeNoiseComputer Science::Computer Vision and Pattern RecognitionsymbolsComputer visionArtificial intelligenceMonochromatic colorCubebusinessAlgorithmMathematics
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Salient Pixels and Dimensionality Reduction for Display of Multi/Hyperspectral Images

2012

International audience; Dimensionality Reduction (DR) of spectral images is a common approach to different purposes such as visualization, noise removal or compression. Most methods such as PCA or band selection use either the entire population of pixels or a uniformly sampled subset in order to compute a projection matrix. By doing so, spatial information is not accurately handled and all the objects contained in the scene are given the same emphasis. Nonetheless, it is possible to focus the DR on the separation of specific Objects of Interest (OoI), simply by neglecting all the others. In PCA for instance, instead of using the variance of the scene in each spectral channel, we show that i…

Spectral Images[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingChannel (digital image)Computer scienceMultispectral image0211 other engineering and technologiesComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingProjection (linear algebra)[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineeringIAPRComputer vision021101 geological & geomatics engineeringSaliencyPixelbusiness.industryDimensionality reductionHyperspectral imagingPattern recognitionDimensionality reductionVisualizationComputer Science::Computer Vision and Pattern Recognition020201 artificial intelligence & image processingArtificial intelligenceFocus (optics)business[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Statistical analysis of engraving traces on a 3D digital model of prehistoric stone stelae

2016

International audience; Studying cultural heritage artefacts, using 3D digital models, is gaining interest. It not only allows applications in documentation and visualisation, but also permits further contact-less examination. In this paper, we are presenting a statistical analysis of stone engravings based on features that were semi-automatically extracted from 3D acquisition data. Our objects of study are two Neolithic stone stelae and a faithful replica that was created in the course of an archaeological study. We use common statistical methods and investigate the populations of depth and diameter of the engraving traces, as well as their correlation. We observe that the erosion of the t…

ArcheologyEngineering[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[SHS.ARCHEO]Humanities and Social Sciences/Archaeology and PrehistoryMaterials Science (miscellaneous)Neolithic stone stelae02 engineering and technologyConservationEngravingPrehistoryChisel marks[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineering0601 history and archaeologyStatistical analysisSpectroscopy060102 archaeology3D mesh databusiness.industryReplica020207 software engineering06 humanities and the artsArchaeologyCultural heritageDescriptive statisticsChemistry (miscellaneous)[ SHS.ARCHEO ] Humanities and Social Sciences/Archaeology and Prehistoryvisual_artStone engravingsvisual_art.visual_art_mediumbusinessGeneral Economics Econometrics and FinanceRegression analysis
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A sensor-data-based denoising framework for hyperspectral images

2015

Many denoising approaches extend image processing to a hyperspectral cube structure, but do not take into account a sensor model nor the format of the recording. We propose a denoising framework for hyperspectral images that uses sensor data to convert an acquisition to a representation facilitating the noise-estimation, namely the photon-corrected image. This photon corrected image format accounts for the most common noise contributions and is spatially proportional to spectral radiance values. The subsequent denoising is based on an extended variational denoising model, which is suited for a Poisson distributed noise. A spatially and spectrally adaptive total variation regularisation term…

Blind deconvolution[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingHyperspectral imagingAnisotropic diffusionComputer scienceNoise reductionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processing02 engineering and technology01 natural sciences010309 opticsOptics[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0103 physical sciencesdenoising0202 electrical engineering electronic engineering information engineeringbusiness.industryHyperspectral imagingcomputer.file_formatNon-local meansAtomic and Molecular Physics and OpticsLight intensityFull spectral imagingComputer Science::Computer Vision and Pattern Recognition020201 artificial intelligence & image processingImage file formatsNoise (video)businesscomputer
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