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14,103
Image denoising by sparse 3D transformdomain collaborative filtering
 IEEE TRANS. IMAGE PROCESS
, 2007
"... We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call “groups.” Collaborative filtering is a special procedure d ..."
Abstract

Cited by 424 (32 self)
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We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call “groups.” Collaborative filtering is a special procedure
Shiftable Multiscale Transforms
, 1992
"... Orthogonal wavelet transforms have recently become a popular representation for multiscale signal and image analysis. One of the major drawbacks of these representations is their lack of translation invariance: the content of wavelet subbands is unstable under translations of the input signal. Wavel ..."
Abstract

Cited by 562 (36 self)
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. Wavelet transforms are also unstable with respect to dilations of the input signal, and in two dimensions, rotations of the input signal. We formalize these problems by defining a type of translation invariance that we call "shiftability". In the spatial domain, shiftability corresponds to a
The Contourlet Transform: An Efficient Directional Multiresolution Image Representation
 IEEE TRANSACTIONS ON IMAGE PROCESSING
"... The limitations of commonly used separable extensions of onedimensional transforms, such as the Fourier and wavelet transforms, in capturing the geometry of image edges are well known. In this paper, we pursue a “true” twodimensional transform that can capture the intrinsic geometrical structure t ..."
Abstract

Cited by 513 (20 self)
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that is key in visual information. The main challenge in exploring geometry in images comes from the discrete nature of the data. Thus, unlike other approaches, such as curvelets, that first develop a transform in the continuous domain and then discretize for sampled data, our approach starts with a discretedomain
Optimal Transform Domain Watermark
"... Optimal transform domain watermark embedding via linear programming PEREIRA, Shelby, VOLOSHYNOVSKYY, Svyatoslav, PUN, Thierry Invisible Digital watermarks have been proposed as a method for discouraging illicit copying and distribution of copyright material. In recent years it has been recognized th ..."
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Optimal transform domain watermark embedding via linear programming PEREIRA, Shelby, VOLOSHYNOVSKYY, Svyatoslav, PUN, Thierry Invisible Digital watermarks have been proposed as a method for discouraging illicit copying and distribution of copyright material. In recent years it has been recognized
TransformDomain Polynomial Filtering
, 1998
"... Polynomial filters are nonlinear systems whose output is a truncated Volterra series expansion of the input or it is the solution of a recursive nonlinear difference equation [1], [2], [3]. In this paper, for implementing polyno mial filters, the framework of transformdomain digital signal process ..."
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Polynomial filters are nonlinear systems whose output is a truncated Volterra series expansion of the input or it is the solution of a recursive nonlinear difference equation [1], [2], [3]. In this paper, for implementing polyno mial filters, the framework of transformdomain digital signal
Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging
 MAGNETIC RESONANCE IN MEDICINE 58:1182–1195
, 2007
"... The sparsity which is implicit in MR images is exploited to significantly undersample kspace. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain–for example, in terms of spatial finit ..."
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Cited by 538 (11 self)
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The sparsity which is implicit in MR images is exploited to significantly undersample kspace. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain–for example, in terms of spatial
Transformdomain WynerZiv codec for video
 in Proceedings of SPIE
"... In current interframe video compression systems, the encoder performs predictive coding to exploit the similarities of successive frames. The WynerZiv Theorem on source coding with side information available only at the decoder suggests that an asymmetric video codec, where individual frames are en ..."
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Cited by 114 (13 self)
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are encoded separately, but decoded conditionally (given temporally adjacent frames) could achieve similar efficiency. We propose a transformdomain WynerZiv coding scheme for motion video that uses intraframe encoding, but interframe decoding. In this system, the transform coefficients of a WynerZiv frame
Transform Domain Analysis of DES
, 1998
"... DES can be regarded as a nonlinear feedback shift register (NLFSR) with input. From this point of view, the tools for pseudorandom sequence analysis are applied to the Sboxes in DES. The properties of the Sboxes of DES under Fourier transform, Hadamard transform, extended Hadamard transform and A ..."
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Cited by 18 (6 self)
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DES can be regarded as a nonlinear feedback shift register (NLFSR) with input. From this point of view, the tools for pseudorandom sequence analysis are applied to the Sboxes in DES. The properties of the Sboxes of DES under Fourier transform, Hadamard transform, extended Hadamard transform
Efficient similarity search in sequence databases
, 1994
"... We propose an indexing method for time sequences for processing similarity queries. We use the Discrete Fourier Transform (DFT) to map time sequences to the frequency domain, the crucial observation being that, for most sequences of practical interest, only the first few frequencies are strong. Anot ..."
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Cited by 515 (19 self)
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We propose an indexing method for time sequences for processing similarity queries. We use the Discrete Fourier Transform (DFT) to map time sequences to the frequency domain, the crucial observation being that, for most sequences of practical interest, only the first few frequencies are strong
Results 1  10
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14,103