Results 41 to 50 of about 1,596,426 (191)
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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Convolutional Sparse Coding for Trajectory Reconstruction [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 Trajectory basis Non-Rigid Structure from Motion (NRSfM) refers to the process of reconstructing the 3D trajectory of each point of a non-rigid object from just their 2D projected trajectories. Reconstruction relies on two factors: (i) the condition of the composed camera & trajectory basis matrix, and (ii) whether the trajectory basis has enough ...
Zhu. Yingying, Lucey, Simon
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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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Collaborative sparse reconstruction for pan-sharpening [PDF]
2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013 In this paper, we extend the Sparse Fusion of Images (SparseFI, pronounced “sparsify”) algorithm, proposed by the authors before, to a Jointly Sparse Fusion of Images (J-SparseFI) algorithm by exploiting the possible signal structural correlations between different multispectral channels. The algorithm is evaluated using airborne UltraCam data.
Zhu, Xiao Xiang +2 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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Scattering Model-Based Frequency-Hopping RCS Reconstruction Using SPICE Methods
Remote Sensing, 2021 RCS reconstruction is an important way to reduce the measurement time in anechoic chambers and expand the radar original data, which can solve the problems of data scarcity and a high measurement cost.
Yingjun Li +4 more
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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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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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Volume Reconstruction from Sparse 3D Ultrasonography [PDF]
, 2003 3D freehand ultrasound has extensive application for organ volume measurement and has been shown to have better reproducibility than estimates of volume made from 2D measurement followed by interpolation to 3D. One key advantage of free-hand ultrasound is that of image compounding, but this advantage is lost in many automated reconstruction systems.
Gooding, M, Kennedy, S, Noble, J
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Group sparse reconstruction using intensity‐based clustering [PDF]
Magnetic Resonance in Medicine, 2012 Compressed sensing has been of great interest to speed up the acquisition of MR images. The k‐t group sparse (k‐t GS) method has recently been introduced for dynamic MR images to exploit not just the sparsity, as in compressed sensing, but also the spatial group structure in the sparse representation.
Prieto, C. +5 more
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