Semi-blind deconvolution of neural impulse response in fMRI using a Gibbs sampling method

Makni, Salima; Ciuciu, Philippe; Idier, Jérôme; Poline, Jean‐Baptiste

doi:10.1109/icassp.2004.1327182

Cited by 8 publications

(9 citation statements)

References 6 publications

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“…where f (y|θ) and f (Φ) have been defined in (5) and (14). The conjugacy of priors in this hierarchical structure allows one to integrate out the parameters σ 2 , and the hyperparameter Φ in the full posterior distribution (15), yielding:…”

Section: Posterior Distributionmentioning

confidence: 99%

Semi-Blind Sparse Image Reconstruction With Application to MRFM

Park

Dobigeon

Hero

2012

IEEE Trans. on Image Process.

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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. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high-dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data.

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Section: Posterior Distributionmentioning

confidence: 99%

Semi-Blind Sparse Image Reconstruction With Application to MRFM

Park

Dobigeon

Hero

2012

IEEE Trans. on Image Process.

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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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“…by solving the discrete counterpart of the EL equation (29) for the discrete image k of using discrete Fourier transforms;…”

Section: A Blind Deconvolution Model With Fractional Regularisationmentioning

confidence: 99%

A new image deconvolution method with fractional regularisation

Williams

Zhang

Chen

2016

Journal of Algorithms & Computational Technology

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Image deconvolution is an important pre-processing step in image analysis which may be combined with denoising, also an important image restoration technique, and prepares the image to facilitate diagnosis in the case of medical images and further processing such as segmentation and registration. Considering the variational approach to this problem, regularisation is a vital component for reconstructing meaningful information and the problem of defining appropriate regularisation is an active research area. An important question in image deconvolution is how to obtain a restored image which has sharp edges where required but also allows smooth regions. Many of the existing regularisation methods allow for one or the other but struggle to obtain good results with both. Consequently, there has been much work in the area of variational image reconstruction in finding regularisation techniques which can provide good quality restoration for images which have both smooth regions and sharp edges. In this paper, we propose a new regularisation technique for image reconstruction in the blind and non-blind deconvolution problems where the precise cause of blur may or may not be known. We present experimental results which demonstrate that this method of regularisation is beneficial for restoring images and blur functions which contain both jumps in intensity and smooth regions.

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Semi-blind deconvolution of neural impulse response in fMRI using a Gibbs sampling method

Cited by 8 publications

References 6 publications

Semi-Blind Sparse Image Reconstruction With Application to MRFM

Semi-Blind Sparse Image Reconstruction With Application to MRFM

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

A new image deconvolution method with fractional regularisation

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