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Results 1 - 10
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43
An EM Algorithm for Wavelet-Based Image Restoration
, 2002
"... This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with low-complexity, expressed in terms of the wavelet coecients, taking a ..."
Abstract
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Cited by 352 (22 self)
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process requiring O(N log N) operations per iteration. Thus, it is the rst image restoration algorithm that optimizes a wavelet-based penalized likelihood criterion and has computational complexity comparable to that of standard wavelet denoising or frequency domain deconvolution methods. The convergence
Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems
- IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
, 2007
"... Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a spa ..."
Abstract
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Cited by 539 (17 self)
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sparseness-inducing (ℓ1) regularization term.Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution, and compressed sensing are a few well-known examples of this approach. This paper proposes gradient projection (GP) algorithms for the bound
Sparse Reconstruction by Separable Approximation
, 2007
"... Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing ..."
Abstract
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Cited by 373 (38 self)
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Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing
Wavelet-based deconvolution for illconditioned systems. Submitted to
- Deparment of Electrical and Computer Engineering, Rice University
"... In this paper, we propose a new approach to wavelet-based de-convolution. Roughly speaking, the algorithm comprises Fourier-domain system inversion followed by wavelet-domain noise sup-pression. Our approach subsumes a number of other wavelet-based deconvolution methods. In contrast to other wavelet ..."
Abstract
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Cited by 28 (4 self)
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In this paper, we propose a new approach to wavelet-based de-convolution. Roughly speaking, the algorithm comprises Fourier-domain system inversion followed by wavelet-domain noise sup-pression. Our approach subsumes a number of other wavelet-based deconvolution methods. In contrast to other
Fast GEM Wavelet-Based Image Deconvolution Algorithm
- IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING-ICIP’03
, 2003
"... The paper proposes a new wavelet-based Bayesian approach to image deconvolution, under the space-invariant blur and additive white Gaussian noise assumptions. Image deconvolution exploits the well known sparsity of the wavelet coefficients, described by heavy-tailed priors. The present approach admi ..."
Abstract
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Cited by 8 (0 self)
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The paper proposes a new wavelet-based Bayesian approach to image deconvolution, under the space-invariant blur and additive white Gaussian noise assumptions. Image deconvolution exploits the well known sparsity of the wavelet coefficients, described by heavy-tailed priors. The present approach
Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation
- JOURNAL OF ZHEJIANG UNIVERSITY - SCIENCE A: APPLIED PHYSICS & ENGINEERING
, 2006
"... In this paper, the inverse problem of reconstructing reflectivity function of a medium is examined within a blind deconvolution framework. The ultrasound pulse is estimated using higher-order statistics, and Wiener filter is used to obtain the ultrasonic reflectivity function through wavelet-based m ..."
Abstract
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Cited by 3 (0 self)
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In this paper, the inverse problem of reconstructing reflectivity function of a medium is examined within a blind deconvolution framework. The ultrasound pulse is estimated using higher-order statistics, and Wiener filter is used to obtain the ultrasonic reflectivity function through wavelet-based
Fast wavelet-based deconvolution of fluorescence micrographs
"... Modern biology depends crucially on two research modalities:1 fluorescent markers and high-resolution mi-croscopy. The need to track biological compounds down to molecular scales poses considerable challenges to the instrumentation. In this context, deconvolution microscopy is becoming a key-element ..."
Abstract
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-element in the experimental process. Wavelet-based deconvolution methods are a recent and promising development.2 However, they have not been considered as a serious alternative to existing deconvolution methods so far, mainly due to their computational cost. Our contribution shows the feasibility of wavelet
Stein block thresholding for wavelet-based image deconvolution
"... Abstract: In this paper, we propose a fast image deconvolution algorithm that combines adaptive block thresholding and Vaguelet-Wavelet Decom-position. The approach consists in first denoising the observed image using a wavelet-domain Stein block thresholding, and then inverting the convo-lution ope ..."
Abstract
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Abstract: In this paper, we propose a fast image deconvolution algorithm that combines adaptive block thresholding and Vaguelet-Wavelet Decom-position. The approach consists in first denoising the observed image using a wavelet-domain Stein block thresholding, and then inverting the convo
Bayesian wavelet-based image deconvolution: A GEM algorithm exploiting a class of heavy-tailed priors
- IEEE Trans. Image Process
, 2006
"... Abstract—Image deconvolution is formulated in the wavelet domain under the Bayesian framework. The well-known sparsity of the wavelet coefficients of real-world images is modeled by heavy-tailed priors belonging to the Gaussian scale mixture (GSM) class; i.e., priors given by a linear (finite of inf ..."
Abstract
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Cited by 67 (9 self)
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Abstract—Image deconvolution is formulated in the wavelet domain under the Bayesian framework. The well-known sparsity of the wavelet coefficients of real-world images is modeled by heavy-tailed priors belonging to the Gaussian scale mixture (GSM) class; i.e., priors given by a linear (finite
Results 1 - 10
of
43
