Results 131 to 140 of about 2,777 (169)

3D discrete curvelet transform

SPIE Proceedings, 2005
In this paper, we present the first 3D discrete curvelet transform. This transform is an extension to the 2D transform described in Candes et al..1 The resulting curvelet frame preserves the important properties, such as parabolic scaling, tightness and sparse representation for singularities of codimension one.
Lexing Ying, Laurent Demanet, Emmanuel Candes   +2 more
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Curvelet based image reconstruction

2008 9th International Conference on Signal Processing, 2008
This paper presents a curvelet based approach for image reconstruction. The objective of the method is to obtain a retrieval image containing as much information as possible for face feature extraction. One attempts have been proposed for the image reconstruction using the wavelet transform.
null Yi Liu, null Xu Cheng
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Curvelet-based multiple prediction

GEOPHYSICS, 2010
The suppression of multiples is a crucial task when processing seismic reflection data. Using the curvelet transform for surface-related multiple prediction is investigated. From a geophysical point of view, a curvelet can be seen as the representation of a local plane wave and is particularly well suited for seismic data decomposition.
Donno, Daniela, Chauris, Hervé, Noble, Mark, S.   +2 more
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Guided waves filtering with warped curvelets

SPIE Proceedings, 2011
Lamb wave testing for SHM is complicated by multimodal propagation and by reflections. In this paper, the effectiveness of the decomposition inWarped Curvelet Frames for the analysis of guided ultrasonic waves is studied. The transformation acts by expanding the analyzed signal into a tight frame of basis functions named Curvelets.
DE MARCHI, LUCA, BARAVELLI, EMANUELE, M. Ruzzene, SPECIALE, NICOLO'ATTILIO   +3 more
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Quaternion Ridgelet Transform and Curvelet Transform

Advances in Applied Clifford Algebras, 2018
The relationships between the Fourier, Radon, wavelet, ridgelet, curvelet transforms for real-valued functions have been extensively studied and are well known. The paper under review extends some of these relationships to quaternion-valued functions. A quaternion \(a\) can be represented as \[ a=a_0+a_1 i+a_2 j+a_3 k, \] with \[ ij=k,\; jk=i,\; ki=j,\;
Ma, Guangsheng, Zhao, Jiman
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Seismic Imaging and Processing with Curvelets

69th EAGE Conference and Exhibition incorporating SPE EUROPEC 2007, 2007
In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation for three problems in seismic processing and imaging namely, seismic data regularization from data with large percentages of traces missing; seismic amplitude recovery for subsalt images obtained by reverse-time migration and primary-multiple separation, given an ...
Herrmann, Felix J., Hennenfent, Gilles, Moghaddam, Peyman P.   +2 more
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Interior tomography with curvelet-based regularization

Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 2016
The interior problem, i.e. reconstruction from local truncated projections in computed tomography (CT), is common in practical applications. However, its solution is non-unique in a general unconstrained setting. To solve the interior problem uniquely and stably, in recent years both the prior knowledge- and compressive sensing (CS)-based methods have
Liu, Baodong, Katsevich, Alexander, Yu, Hengyong   +2 more
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Cartoon Approximation with $��$-Curvelets

2014
It is well-known that curvelets provide optimal approximations for so-called cartoon images which are defined as piecewise $C^2$-functions, separated by a $C^2$ singularity curve. In this paper, we consider the more general case of piecewise $C^ $-functions, separated by a $C^ $ singularity curve for $ \in (1,2]$.
Grohs, Philipp, Keiper, Sandra, Kutyniok, Gitta, Sch��fer, Martin   +3 more
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Multiscale Dynamic Curvelet Scattering Network

IEEE Transactions on Neural Networks and Learning Systems
The feature representation learning process greatly determines the performance of networks in classification tasks. By combining multiscale geometric tools and networks, better representation and learning can be achieved. However, relatively fixed geometric features and multiscale structures are always used.
Jie Gao, Licheng Jiao, Xu Liu, Lingling Li, Puhua Chen, Fang Liu, Shuyuan Yang   +6 more
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Curvelets for surface characterization

Applied Physics Letters, 2007
Surface metrology is the science of measuring small-scale features on surfaces, which is important to many disciplines including tribology, fluid mechanics, optics, and manufacturing. Applications of wavelets on functional surfaces have become an increasing interest.
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