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IEEE Signal Processing Letters, ISSN 1070-9908, 12/2007, Volume 14, Issue 12, pp. 936 - 939
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 04/2017, Volume 39, Issue 4, pp. 818 - 832
Journal Article
IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, 06/2011, Volume 49, Issue 6, pp. 2251 - 2258
Journal Article
ASTRONOMY & ASTROPHYSICS, ISSN 1432-0746, 05/2016, Volume 589, p. A54
Context. Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or... 
POINT-SPREAD FUNCTION | methods: data analysis | planetary systems | techniques: image processing | STATISTICS | DISKS | ASTRONOMY & ASTROPHYSICS | techniques: high angular resolution | BETA-PICTORIS | planets and satellites: detection | Algorithms | Noise | Imaging | Projection | Principal components analysis | Decomposition | Subspaces | Standards
Journal Article
Linear Algebra and Its Applications, ISSN 0024-3795, 2010, Volume 433, Issue 11, pp. 1851 - 1872
We present an algorithm for decomposing a symmetric tensor, of dimension n and order d, as a sum of rank-1 symmetric tensors, extending the algorithm of... 
Tensor decomposition | MATHEMATICS, APPLIED | JOINT DIAGONALIZATION | ALGORITHM | RANK | BLIND IDENTIFICATION | MIXTURES | MATHEMATICS | 3-WAY ARRAYS | COMPLEXITY | PARAFAC | SEPARATION | SQUARES | Algorithms | Algebraic Geometry | Mathematics | Symbolic Computation | Computer Science
Journal Article
Neurocomputing, ISSN 0925-2312, 11/2019, Volume 366, pp. 264 - 275
Image decomposition is one of the essential image processing techniques in computer vision and computational photography because it can be applied to various... 
Total variation | Low rank matrix factorization | Image decomposition | TEXTURE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
Medical Physics, ISSN 0094-2405, 04/2014, Volume 41, Issue 4, pp. 041901 - n/a
Purpose: Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its capability of material decomposition. Direct... 
conjugate gradient methods | iterative methods | Medical image noise | Medical image smoothing | noise suppression | Density measurement | Noise | Matrix theory | phantoms | Digital computing or data processing equipment or methods, specially adapted for specific applications | Computed tomography | Medical X‐ray imaging | best linear unbiased estimation | edge detection | edge predetection | Computerised tomographs | image resolution | medical image processing | covariance matrices | least squares approximations | Medical imaging | Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image | Numerical linear algebra | matrix inversion | Biological material, e.g. blood, urine; Haemocytometers | image denoising | image‐domain decomposition | computerised tomography | Image data processing or generation, in general | Medical image reconstruction | dual‐energy CT | General statistical methods | Medical image spatial resolution | Spatial resolution | Edge predetection | Dual-energy CT | Image-domain decomposition | Best linear unbiased estimation | Noise suppression | dual-energy CT | INITIAL-EXPERIENCE | EFFECTIVE ATOMIC NUMBER | LEAST-SQUARES APPROACH | SCATTER CORRECTION | COMPUTED-TOMOGRAPHY | image-domain decomposition | RADIATION-THERAPY | CONE-BEAM CT | EDGE-DETECTION | NOISE-REDUCTION | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | ELECTRON-DENSITY | Head - diagnostic imaging | Tomography, X-Ray Computed - methods | Algorithms | Image Processing, Computer-Assisted - methods | Phantoms, Imaging | Signal-To-Noise Ratio | SPATIAL RESOLUTION | DECOMPOSITION | ALGORITHMS | COMPUTERIZED TOMOGRAPHY | IMAGE PROCESSING | SIGNAL-TO-NOISE RATIO | INHIBITION | LEAST SQUARE FIT | IMAGES | RADIOLOGY AND NUCLEAR MEDICINE | PHANTOMS | ITERATIVE METHODS | SMOOTH MANIFOLDS
Journal Article
Pattern Recognition, ISSN 0031-3203, 05/2017, Volume 65, pp. 211 - 222
Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems such as image recognition.... 
Heatmapping | Image recognition | Deep neural networks | Taylor decomposition | Relevance propagation | Heaimapping | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Electrical engineering | Computer science | Neural networks | Analysis | Machine learning
Journal Article
IEEE Transactions on Signal Processing, ISSN 1053-587X, 2009, Volume 57, Issue 1, pp. 289 - 301
In this paper, a fast algorithm for overcomplete sparse decomposition, called SL0, is proposed. The algorithm is essentially a method for obtaining sparse... 
Sparse component analysis (SCA) | Sparse source separation | Overcomplete signal representation | Sparse decomposition | Blind source separation (BSS) | Compressed sensing | Atomic decomposition
Journal Article
IEEE Transactions on Signal Processing, ISSN 1053-587X, 01/2008, Volume 56, Issue 1, pp. 85 - 95
Journal Article