Results 21 to 30 of about 1,177,560 (190)
Sparse signal reconstruction by swarm intelligence algorithms
Engineering Science and Technology, an International Journal, 2021 This study introduces a new technique for sparse signal reconstruction. In general, there are two classes of algorithms in the recovery of sparse signals: greedy approaches and l1-minimization methods. The proposed method employs swarm intelligence based
Murat Emre Erkoç, Nurhan Karaboğa
doaj +1 more source
SMALLbox - An Evaluation Framework for Sparse Representations and Dictionary Learning Algorithms [PDF]
, 2010 International ...
Plumbley, Mark D. +8 more
core +5 more sources
Sparse Image Reconstruction for Molecular Imaging [PDF]
IEEE Transactions on Image Processing, 2009 12 pages, 8 ...
Michael Ting +2 more
openaire +4 more sources
Hierarchical Bayesian sparse image reconstruction with application to MRFM [PDF]
, 2008 This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally
Hero, Alfred O. +2 more
core +1 more source
Remote Sensing, 2019 The magnetic resonance sounding (MRS) method is a non-invasive, efficient and advanced geophysical method for groundwater detection. However, the MRS signal received by the coil sensor is extremely susceptible to electromagnetic noise interference.
Xiaokang Yao +4 more
doaj +1 more source
Combinatorial Regression and Improved Basis Pursuit for Sparse Estimation [PDF]
, 2012 Sparse representations accurately model many real-world data sets. Some form of sparsity is conceivable in almost every practical application, from image and video processing, to spectral sensing in radar detection, to bio-computation and genomic signal ...
Khajehnejad, M. Amin
core +1 more source
Kernel Reconstruction ICA for Sparse Representation [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2015 Independent component analysis with soft reconstruction cost (RICA) has been recently proposed to linearly learn sparse representation with an overcomplete basis, and this technique exhibits promising performance even on unwhitened data. However, linear RICA may not be effective for the majority of real-world data because nonlinearly separable data ...
Yanhui Xiao +4 more
openaire +3 more sources
Semi-blind sparse image reconstruction with application to MRFM [PDF]
, 2012 We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known.
Hero, Alfred O. +2 more
core +1 more source
Color Image Super-resolution Reconstruction Based on Color Constraint and Nonlocal Sparse Representation [PDF]
Jisuanji gongcheng, 2019 Color image super-resolution reconstruction method based on sparse representation model usually adopts sparse coding process based on image blocks,which easily leads to the instability of sparse representation,and the problems of detail blurring and ...
XU Zhigang, MA Qiang, ZHU Honglei, ZHANG Moyi
doaj +1 more source
Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]
, 2014 We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Se Un Parka +5 more
core +1 more source