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Nonlocal Elastica Model for Sparse Reconstruction
Journal of Mathematical Imaging and Vision, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mengyuan Yan, Yuping Duan
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Sparse reconstruction of ISOMAP representations
Journal of Intelligent & Fuzzy Systems, 2019Isometric feature mapping (ISOMAP) is one of the classical methods of nonlinear dimensionality reduction (NLDR) and seeks for low dimensional (LD) structure of high dimensional (HD) data. However, the inverse problem of ISOMAP has never been investigated, which recovers the HD sample from the related LD sample, and its application prospect in data ...
Honggui Li, Maria Trocan
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Sparse correlation kernel reconstruction
1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999This paper presents a new paradigm for signal reconstruction and superresolution, correlation kernel analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class-specific basis functions. The basis functions that we use are the correlation functions of the class of signals we are analyzing.
Constantine Papageorgiou +2 more
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A recursive approach to reconstruction of sparse signals
2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014Compressive Sensing (CS) theory details how a sparsely represented signal in a known basis can be reconstructed using less number of measurements. In many practical systems, the observation signal has a sparse representation in a continuous parameter space. This situation rises the possibility of use of the CS reconstruction techniques in the practical
Oguzhan Teke +2 more
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Sparse reconstruction for radar
SPIE Proceedings, 2008Imaging is not itself a system goal, but is rather a means to support inference tasks. For data processing with linearized signal models, we seek to report all high-probability interpretations of the data and to report confidence labels in the form of posterior probabilities.
Lee C. Potter +2 more
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Wideband Sparse Reconstruction for Scanning Radar
IEEE Transactions on Geoscience and Remote Sensing, 2018Recently, the generalized sparse iterative covariance-based estimation algorithm was extended to allow for varying norm constraints in scanning radar applications. In this paper, further to this development, we introduce a wideband dictionary framework which can provide a computationally efficient estimation of sparse signals.
Yongchao Zhang 0001 +4 more
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Sparse angle CT reconstruction based on group sparse representation
Journal of X-Ray Science and Technology, 2022OBJECTIVE: In order to solve the problem of image quality degradation of CT reconstruction under sparse angle projection, we propose to develop and test a new sparse angle CT reconstruction method based on group sparse. METHODS: In this method, the group-based sparse representation is introduced into the statistical iterative reconstruction framework ...
Yanan, Gu +5 more
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A spline framework for sparse tomographic reconstruction
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013We present a spline-based sparse tomographic reconstruction framework. The proposed method utilizes the closed-form analytical Radon transform of B-splines and box splines of any order and integrates the (transform-domain) sparsity of the image into the reconstruction algorithm.
Mahsa Mirzargar +2 more
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An Evolutionary Multiobjective Approach to Sparse Reconstruction
IEEE Transactions on Evolutionary Computation, 2014This paper addresses the problem of finding sparse solutions to linear systems. Although this problem involves two competing cost function terms (measurement error and a sparsity-inducing term), previous approaches combine these into a single cost term and solve the problem using conventional numerical optimization methods.
Lin Li 0016 +4 more
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Group sparse reconstruction for image segmentation
Neurocomputing, 2014Abstract Image segmentation is a fundamental problem in computer vision and image analysis. Specially, the segmentation of medical images can assist doctors in making decisions. Due to the lack of distinctive features to describe the boundary of an organ and match function with high performance for features, medical image segmentation is difficult to
Xiaoqiang Lu, Xuelong Li 0001
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