Myopic deconvolution combining Kalman filter and tracking control

Sarri, Patrick; Thomas, Gérard; Sekko, Edgard; Neveux, Philippe

doi:10.1109/icassp.1998.681819

Cited by 6 publications

(7 citation statements)

References 7 publications

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“…11 visualizes the reconstruction error performance for several measures of error. From these figures we conclude that the VB semiblind algorithm performs at least as well as the previous MCMC semi-blind al- 6 Note that the 0 norm has been normalized. The true image has value 1; x 0 / x 0 is used for MCMC method; E [w] × N/ x 0 for variational method since this method does not produce zero pixels but E [w].…”

Section: Comparison With Other Algorithmsmentioning

confidence: 68%

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Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM

Park¹,

Dobigeon²,

Hero³

2014

Signal Processing

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International audienceWe 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. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM)

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Section: Comparison With Other Algorithmsmentioning

confidence: 68%

“…To deal with this mismatch, deconvolution methods have been proposed to estimate the unknown image and the PSF jointly. When prior knowledge of the PSF is available, these methods are usually referred to as semi-blind deconvolution [4,5] or myopic deconvolution [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM

Park¹,

Dobigeon²,

Hero³

2014

Signal Processing

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“…We have also demonstrated that these joint models can obtain improved performance over comparable two-stage models. This model can be further extended to the semi-blind case where some information about the blur function may be assumed to be known [38][39][40][41][42][43] which allows for increases in speed and to work with multi-channel images. This model will be considered for selective segmentation 9 and vessel segmentation techniques among others.…”

Section: Discussionmentioning

confidence: 99%

An effective variational model for simultaneous reconstruction and segmentation of blurred images

Williams

Spencer

Chen

et al. 2016

Journal of Algorithms & Computational Technology

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The segmentation of blurred images is of great importance. There have been several recent pieces of work to tackle this problem and to link the areas of image segmentation and image deconvolution in the case where the blur function is known or of known type, such as Gaussian, but not in the case where the blur function is not known due to a lack of robust blind deconvolution methods. Here we propose two variational models for simultaneous reconstruction and segmentation of blurred images with spatially invariant blur, without assuming a known blur or a known blur type. Based on our recent work in blind deconvolution, we present two solution methods for the segmentation of blurred images based on implicitly constrained image reconstruction and convex segmentation. The first method is aimed at obtaining a good quality segmentation while the other is aimed at improving the speed while retaining the quality. Our results demonstrate that, while existing models are capable of segmenting images corrupted by small amounts of blur, they begin to struggle when faced with heavy blur degradation or noise, due to the limitation of edge detectors or a lack of strict constraints. We demonstrate that our new algorithms are effective for segmenting blurred images without prior knowledge of the blur function, in the presence of noise and offer improved results for images corrupted by strong blur.

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“…In such circumstances, the PSF used in the reconstruction algorithm is mismatched to the true PSF and the quality of standard image reconstruction technique will suffer if one does not account for this mismatch. Estimating the unknown image and the PSF jointly is usually referred to as semi-blind 14,15 or myopic 16,17 deconvolution, and this is the approach taken in this paper.…”

Section: 12mentioning

confidence: 99%

Myopic sparse image reconstruction with application to MRFM

2011

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We propose a solution to the image deconvolution problem where the convolution operator or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the uncertainty in a high dimensional space. Specifically, we assume the image is sparse corresponding to the natural sparsity of magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian myopic algorithm is superior to previously proposed algorithms such as the alternating minimization (AM) algorithm for sparse images. We illustrate our myopic algorithm on real MRFM tobacco virus data.

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Myopic deconvolution combining Kalman filter and tracking control

Cited by 6 publications

References 7 publications

Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM

Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM

An effective variational model for simultaneous reconstruction and segmentation of blurred images

Myopic sparse image reconstruction with application to MRFM

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