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- 摘要
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- 实验方案
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Fast 3D image reconstruction by cuboids and 3D Charlier’s moments
摘要： In this article, we propose a novel approach to accelerate the processing of 3D images by the discrete orthogonal moments of Charlier. The proposed approach is based on two fundamental notions: The first is the acceleration of the computing time of Charlier discrete orthogonal polynomials and moments in the case of the 3D image using digital filters. The second is the description of the 3D image by a set of cuboids of fixed size instead of individual voxels by decomposing the image by cuboids of small sizes to ensure numerical stability. By applying this method, the 3D Charlier moments are calculated from the cuboids instead of the whole image, as the image processing will be locally in each cuboid. This method allows us to speed up the computation time of the moments and to avoid the problem of propagation of digital errors encountered as well when using of digital filters for 3D images of large sizes. The simulation results show the effectiveness of the proposed method in terms of the computation time of the 3D moments of Charlier and in terms of quality of 3D image.
关键词： 3D Charlier moments,Digital filters,3D image reconstruction,3D image cuboid representation
更新于2025-09-23 15:23:52
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Lower Power, Better Uniformity, and Stability CBRAM Enabled by Graphene Nanohole Interface Engineering
摘要： With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.
关键词： image reconstruction,image representations,Adaptive filters,image edge analysis,image enhancement,synthetic aperture radar (SAR),image analysis,digital filters
更新于2025-09-23 15:21:01
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A Phase Calibration Method for Millimeter-Wave Up-Converter Using Electro-Optic Sampling
摘要： With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.
关键词： Adaptive filters,digital filters,image analysis,image reconstruction,image representations,image edge analysis,image enhancement,synthetic aperture radar (SAR)
更新于2025-09-19 17:13:59
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Dual-Core Photonic Crystal Fiber-Based Plasmonic RI Sensor in the Visible to Near-IR Operating Band
摘要： With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.
关键词： Adaptive filters,digital filters,image analysis,image reconstruction,image representations,image edge analysis,image enhancement,synthetic aperture radar (SAR)
更新于2025-09-19 17:13:59