Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging [PDF]
Sensors (Basel), 2016 Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging.
Yichun Zhang +6 more
core +2 more sources
Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images [PDF]
IEEE Transactions on Medical Imaging, 2017 David Cunefare, Shutao Li, Leyuan Fang
exaly +2 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
Identification of Matrices Having a Sparse Representation [PDF]
, 2008 We consider the problem of recovering a matrix from its action on a known vector in the setting where the matrix can be represented efficiently in a known matrix dictionary.
Pfander, Goetz E. +6 more
core +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
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
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
Convex Analysis for Minimizing and Learning Submodular Set Functions [PDF]
, 2013 The connections between convexity and submodularity are explored, for purposes of minimizing and learning submodular set functions. First, we develop a novel method for minimizing a particular class of submodular functions, which can be expressed as a
Peter Stobbe, Stobbe, Peter
core +1 more source
Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction.
, 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 (7776104) +8 more
core +5 more sources
Bayesian Orthogonal Component Analysis for Sparse Representation [PDF]
, 2010 This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This undercomplete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with ...
Nicolas Dobigeon +3 more
core +1 more source