Results 31 to 40 of about 1,177,560 (190)
Identification of Matrices Having a Sparse Representation [PDF]
, 2008 We consider the problem of recovering a matrix from its action on a known vector in the setting where the matrix can be represented efficiently in a known matrix dictionary.
Pfander, Goetz E. +6 more
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Bayesian Orthogonal Component Analysis for Sparse Representation [PDF]
, 2010 This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This undercomplete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with ...
Nicolas Dobigeon +3 more
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Sparse ACEKF for phase reconstruction
Optics Express, 2013 We propose a novel low-complexity recursive filter to efficiently recover quantitative phase from a series of noisy intensity images taken through focus. We first transform the wave propagation equation and nonlinear observation model (intensity measurement) into a complex augmented state space model.
Jingshan, Zhong +3 more
openaire +5 more sources
Sparse-View Ct Reconstruction Via Convolutional Sparse Coding [PDF]
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019 Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features. To deal with these problems, the convolutional sparse coding (CSC) has been proposed and introduced into various applications.
Peng Bao +4 more
openaire +2 more sources
New Directions In Sparse Sampling and Estimation For Underdetermined Systems [PDF]
, 2013 A central objective in signal processing is to infer meaningful information from a set of measurements or data. While most signal models have an overdetermined structure (the number of unknowns less than the number of equations), traditionally very few ...
Piya Pal, Pal, Piya
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Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction
International Journal of Antennas and Propagation, 2017 Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse
Dong Zhang, Yongshun Zhang, Cunqian Feng
doaj +1 more source
Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning
Sensors, 2022 A reconstruction algorithm is proposed, based on multi-dictionary learning (MDL), to improve the reconstruction quality of acoustic tomography for complex temperature fields.
Yuankun Wei, Hua Yan, Yinggang Zhou
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Exact CS Reconstruction Condition of Undersampled Spectrum-Sparse Signals
Journal of Applied Mathematics, 2013 Compressive sensing (CS) reconstruction of a spectrum-sparse signal from undersampled data is, in fact, an ill-posed problem. In this paper, we mathematically prove that, in certain cases, the exact CS reconstruction of a spectrum-sparse signal from ...
Ying Luo +3 more
doaj +1 more source
Extreme Learning Machines as Encoders for Sparse Reconstruction
Fluids, 2018 Reconstruction of fine-scale information from sparse data is often needed in practical fluid dynamics where the sensors are typically sparse and yet, one may need to learn the underlying flow structures or inform predictions through assimilation into ...
S M Abdullah Al Mamun +2 more
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The cosparse analysis model and algorithms [PDF]
, 2013 After a decade of extensive study of the sparse representation synthesis model, we can safely say that this is a mature and stable field, with clear theoretical foundations, and appealing applications.
Gribonval, Rémi +8 more
core +1 more source