0000000000860607

AUTHOR

Pier Luigi Dragotti

showing 3 related works from this author

Directionlets: Anisotropic Multidirectional representation with separable filtering

2006

In spite of the success of the standard wavelet transform (WT) in image processing in recent years, the efficiency of its representation is limited by the spatial isotropy of its basis functions built in the horizontal and vertical directions. One-dimensional (1-D) discontinuities in images (edges and contours) that are very important elements in visual perception, intersect too many wavelet basis functions and lead to a nonsparse representation. To efficiently capture these anisotropic geometrical structures characterized by many more than the horizontal and vertical directions, a more complex multidirectional (M-DIR) and anisotropic transform is required. We present a new lattice-based pe…

geometrysparse image representationMultiresolution analysisInformation Storage and RetrievalGeometryBasis functionDirectional vanishing momentsseparable filteringwaveletsWaveletmultiresolutionImage Interpretation Computer-AssistedComputer GraphicsCurveletComputer SimulationmultidirectionMathematicsStochastic ProcessesModels StatisticalMathematical analysisWavelet transformfilter banksNumerical Analysis Computer-AssistedSignal Processing Computer-AssistedImage EnhancementFilter bankComputer Graphics and Computer-Aided DesignContourletFilter designAnisotropyArtifactsdirectionletsAlgorithmsFiltrationSoftware
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Low-Rate Reduced Complexity Image Compression using Directionlets

2006

The standard separable two-dimensional (2-D) wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to capture efficiently one-dimensional (1-D) discontinuities, like edges and contours, that are anisotropic and characterized by geometrical regularity along different directions. In our previous work, we proposed a construction of critically sampled perfect reconstruction anisotropic transform with directional vanishing moments (DVM) imposed in the corresponding basis functions, called directionlets. Here, we show that the computational complexity of our transform is comparable to the co…

Computational complexity theorybusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage codingWavelet transformPattern recognitionImage processingImage segmentationSparse approximationWavelet transformsWaveletData compressionImage reconstructionArtificial intelligencebusinessImage representationMathematicsImage compressionData compression2006 International Conference on Image Processing
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Sparse Image Representation by Directionlets

2010

Despite the success of the standard wavelet transform (WT) in image processing in recent years, the efficiency and sparsity of its representation are limited by the spatial symmetry and separability of its basis functions built in the horizontal and vertical directions. One-dimensional discontinuities in images (edges or contours), which are important elements in visual perception, intersect too many wavelet basis functions and lead to a non-sparse representation. To capture efficiently these elongated structures characterized by geometrical regularity along different directions (not only the horizontal and vertical), a more complex multidirectional (M-DIR) and asymmetric transform is requi…

Directional transformsbusiness.industryMultiresolution analysisWavelet transformImage codingImage processingDirectional vanishing momentsContourletImage orientation analysisWavelet transformsWaveletCurveletImage scalingImage interpolationComputer visionSeparable transformsArtificial intelligencebusinessAlgorithmMultiresolution analysisSparse representationMathematicsImage compression
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