Search results for "Directional vanishing moments"

showing 3 items of 3 documents

Space-Frequency Quantization for Image Compression With Directionlets

2007

The standard separable 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 efficiently capture 1-D discontinuities, like edges or contours. These features, being elongated and characterized by geometrical regularity along different directions, intersect and generate many large magnitude wavelet coefficients. Since contours are very important elements in the visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. In our previous work, we proposed a construction of critic…

image orientation analysisMultiresolution analysisVideo RecordingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingnonseparable transformsmultiresolution analysisRate–distortion theoryWaveletDVMsImage Interpretation Computer-AssistedComputer GraphicsComputer visionQuantization (image processing)image codingimage segmentationMathematicsbusiness.industryWavelet transformNumerical Analysis Computer-AssistedSignal Processing Computer-AssistedWTsData CompressionImage EnhancementComputer Graphics and Computer-Aided Designwavelet transformsdirectional vanishing momentsdirectional transformsArtificial intelligencebusinessAlgorithmsSoftwareImage compressionData compressionIEEE Transactions on Image Processing
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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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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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