Results 61 to 70 of about 1,596,426 (191)

A Dual-Domain Diffusion Model for Sparse-View CT Reconstruction

IEEE Signal Processing Letters
To reduce the radiation dose, sparse-view computed tomography (CT) reconstruction has been proposed, aiming to recover high-quality CT images from sparsely sampled sinogram.
Chun Yang, Dian Sheng, Bo Yang, Wenfeng Zheng, Chao Liu   +4 more
semanticscholar   +1 more source

Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction

Scientific Reports, 2018
Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with both accelerated scan and reduced projection/back-projection calculation.
Shipeng Xie, Xinyu Zheng, Yang Chen, Lizhe Xie, Jin Liu, Yudong Zhang, Jingjie Yan, Hu Zhu, Yining Hu   +8 more
doaj   +1 more source

Nonparametric Statistical Thresholding for Sparse Magnetoencephalography Source Reconstructions

Frontiers in Neuroscience, 2012
Uncovering brain activity from MEG data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources.
Julia Parsons Owen, Julia Parsons Owen, Kensuke eSekihara, Srikantan S. Nagarajan, Srikantan S. Nagarajan   +4 more
doaj   +1 more source

An extremum-guided interpolation for sparsely sampled photoacoustic imaging

Photoacoustics, 2023
In photoacoustic (PA) reconstruction, spatial constraints or real-time system requirements often result to sparse PA sampling data. For sparse PA sensor data, the sparse spatial and dense temporal sampling often leads to poor signal continuity.
Haoyu Wang, Luo Yan, Cheng Ma, Yiping Han   +3 more
doaj   +1 more source

Atmospheric Inverse Modeling via Sparse Reconstruction

Geoscientific Model Development, 2016
Abstract. Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior.
Nils Hase, Scot M. Miller, Peter Maaß, Justus Notholt, Mathias Palm, Thorsten Warneke   +5 more
openaire   +3 more sources

A Sparse Representation-Based Reconstruction Method of Electrical Impedance Imaging for Grounding Grid

Energies
As a non-invasive imaging method, electrical impedance tomography (EIT) technology has become a research focus for grounding grid corrosion diagnosis. However, the existing algorithms have not produced ideal image reconstruction results.
Ke Zhu, Donghui Luo, Zhengzheng Fu, Zhihang Xue, Xianghang Bu   +4 more
doaj   +1 more source

Study on the Sparse Sub-block Microwave Imaging Based on Lasso(In English)

Leida xuebao, 2013
Sparse microwave imaging requires nonlinear algorithm that is expensive for large scene imaging. Therefore, the sub-block imaging method is studied, in which the measured data and the relative imaging region is divided into sub-blocks, and then sparse ...
Xiang Yin, Zhang Bing-chen, Hong Wen
doaj   +1 more source

Compressive Reconstruction Based on Sparse Autoencoder Network Prior for Single-Pixel Imaging

Photonics, 2023
The combination of single-pixel imaging and single photon-counting technology enables ultra-high-sensitivity photon-counting imaging. In order to shorten the reconstruction time of single-photon counting, the algorithm of compressed sensing is used to ...
Hong Zeng, Jiawei Dong, Qianxi Li, Weining Chen, Sen Dong, Huinan Guo, Hao Wang   +6 more
doaj   +1 more source

Structure-Based Bayesian Sparse Reconstruction [PDF]

IEEE Transactions on Signal Processing, 2012
Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates.
Quadeer, Ahmed Abdul, Al-Naffouri, Tareq Y.   +1 more
openaire   +3 more sources

Sparse reconstruction with multiple Walsh matrices [PDF]

Advances in Pure and Applied Mathematics, 2019
Abstract The problem of how to find a sparse representation of a signal is an important one in applied and computational harmonic analysis. It is closely related to the problem of how to reconstruct a sparse vector from its projection in a much lower-dimensional vector space.
openaire   +3 more sources