Results 41 to 50 of about 1,177,560 (190)
GRADIENT POLYTOPE FACES PURSUIT FOR LARGE SCALE SPARSE RECOVERY PROBLEMS [PDF]
, 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Dallas, TX, 14-19 March ...
Plumbley, Mark D. +8 more
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Sparse poisson intensity reconstruction algorithms [PDF]
2009 IEEE/SP 15th Workshop on Statistical Signal Processing, 2009 The observations in many applications consist of counts of discrete events, such as photons hitting a dector, which cannot be effectively modeled using an additive bounded or Gaussian noise model, and instead require a Poisson noise model. As a result, accurate reconstruction of a spatially or temporally distributed phenomenon (f) from Poisson data (y)
Harmany, Zachary T. +2 more
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Adaptive thresholding for sparse image reconstruction
Telfor Journal, 2023 The performance of the class of sparse reconstruction algorithms which is based on the iterative thresholding is highly dependent on a selection of the appropriate threshold value, controlling a trade-off between the algorithm execution time and the solution accuracy. This is why most of the state-of-the-art reconstruction algorithms employ some method
Volarić, Ivan, Sučić, Viktor
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Image Saliency Detection Combining Sparse Reconstruction and Compactness
Jisuanji kexue yu tansuo, 2020 Aiming at the problem that existing image saliency detection algorithms can't correctly detect salient objects in complex environments, this paper proposes a method combining sparse reconstruction error and the compactness of image salient regions to ...
ZHANG Yingying, GE Hongwei
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Computed Tomography Reconstruction Algorithm Based on Relative Total Variation Minimization
CT Lilun yu yingyong yanjiu, 2023 The total variation (TV) minimization algorithm is an effective CT image reconstruction algorithm that can reconstruct sparse or noisy projection data with high accuracy. However, in some cases, the TV algorithm produces stepped artifacts.
Jiahao ZHANG, Zhiwei QIAO
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Iterative Forward-Backward Pursuit Algorithm for Compressed Sensing
Journal of Electrical and Computer Engineering, 2016 It has been shown that iterative reweighted strategies will often improve the performance of many sparse reconstruction algorithms. Iterative Framework for Sparse Reconstruction Algorithms (IFSRA) is a recently proposed method which iteratively enhances ...
Feng Wang +3 more
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Neural 3D reconstruction from sparse views using geometric priors
Computational Visual Media, 2023 Sparse view 3D reconstruction has attracted increasing attention with the development of neural implicit 3D representation. Existing methods usually only make use of 2D views, requiring a dense set of input views for accurate 3D reconstruction.
Tai-Jiang Mu +3 more
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EPRI Sparse-reconstruction Method Based on Deep Learning
CT Lilun yu yingyong yanjiuElectron paramagnetic resonance imaging (EPRI) is an advanced method for imaging tumor oxygen concentration. The current bottleneck of EPRI is the extremely long scanning time.
Congcong DU, Zhiwei QIAO
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Split Bregman Algorithm for Structured Sparse Reconstruction
IEEE Access, 2018 Sparse reconstruction has attracted considerable attention in recent years and shown powerful capabilities in many applications. In standard sparse reconstruction, the sparse nonzero elements appear anywhere in a vector.
Jian Zou, Haifeng Li, Guoqi Liu
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Plug-and-Play Priors for Model Based Reconstruction [PDF]
, 2013 Model-based reconstruction is a powerful framework for solving a variety of inverse problems in imaging. The method works by combining a forward model of the imaging system with a prior model of the image itself, and the reconstruction is then computed ...
Bouman, Charles A. +2 more
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