Image reconstruction exploiting object sparsity in boundary-enhanced X-ray phase-contrast tomography

Sidky, Emil Y.; Anastasio, Mark A.; Pan, Xiaochuan

doi:10.1364/oe.18.010404

Cited by 48 publications

(54 citation statements)

References 40 publications

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“…Δn indicates the estimate for the index perturbation due to the object. The term SΔn is a sparsity constraint [27][28][29] to enhance the contrast, while τ is a parameter that can be tuned to maximize image quality by systematic search.…”

Section: Methodsmentioning

confidence: 99%

Learning approach to optical tomography

Kamilov

Papadopoulos

Shoreh

et al. 2015

Optica

337

190

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Optical tomography has been widely investigated for biomedical imaging applications. In recent years optical tomography has been combined with digital holography and has been employed to produce high-quality images of phase objects such as cells. In this paper we describe a method for imaging 3D phase objects in a tomographic configuration implemented by training an artificial neural network to reproduce the complex amplitude of the experimentally measured scattered light. The network is designed such that the voxel values of the refractive index of the 3D object are the variables that are adapted during the training process. We demonstrate the method experimentally by forming images of the 3D refractive index distribution of Hela cells.

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Section: Methodsmentioning

confidence: 99%

Learning approach to optical tomography

Kamilov

Papadopoulos

Shoreh

et al. 2015

Optica

337

190

View full text Add to dashboard Cite

show abstract

“…However, little effort has been devoted to dealing with the spectral correlation. Recently, the sparse representation technique has been successfully applied in denoising problems [15][16][17][18]. It is based on the assumption that the noise-free image can be estimated by a linear combination of a few atoms in a redundant dictionary, which can be learned by utilizing the correlation and redundancy in an image [19].…”

Section: Introductionmentioning

confidence: 99%

Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation

Zhou

Wang

Huo

et al. 2016

Journal of the Optical Society of Korea

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Hyperspectral images are often contaminated with stripe noise, which severely degrades the imaging quality and the precision of the subsequent processing. In this paper, a variational model is proposed by employing spectral-spatial adaptive unidirectional variation and a sparse representation. Unlike traditional methods, we exploit the spectral correction and remove stripes in different bands and different regions adaptively, instead of selecting parameters band by band. The regularization strength adapts to the spectrally varying stripe intensities and the spatially varying texture information. Spectral correlation is exploited via dictionary learning in the sparse representation framework to prevent spectral distortion. Moreover, the minimization problem, which contains two unsmooth and inseparable l1-norm terms, is optimized by the split Bregman approach. Experimental results, on datasets from several imaging systems, demonstrate that the proposed method can remove stripe noise effectively and adaptively, as well as preserve original detail information.

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“…It was initially developed for image denoising [22] and recently been introduced in such fields as deblurring, super-resolution, inpainting and tomography [23][24][25][26]. The idea underlying TV minimization is to promote a solution that has the sparsest gradient.…”

Section: Introductionmentioning

confidence: 99%

Phase retrieval in in-line x-ray phase contrast imaging based on total variation minimization

Kostenko¹,

Batenburg²,

Suhonen³

et al. 2013

Opt. Express

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State-of-the-art techniques for phase retrieval in propagation based X-ray phase-contrast imaging are aiming to solve an underdetermined linear system of equations. They commonly employ Tikhonov regularization − an L2-norm regularized deconvolution scheme − despite some of its limitations. We present a novel approach to phase retrieval based on Total Variation (TV) minimization. We incorporated TV minimization for deconvolution in phase retrieval using a variety of the most common linear phase-contrast models. The results of our TV minimization was compared with Tikhonov regularized deconvolution on simulated as well as experimental data. The presented method was shown to deliver improved accuracy in reconstructions based on a single distance as well as multiple distance phase-contrast images corrupted by noise and hampered by errors due to nonlinear imaging effects.

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Image reconstruction exploiting object sparsity in boundary-enhanced X-ray phase-contrast tomography

Cited by 48 publications

References 40 publications

Learning approach to optical tomography

Learning approach to optical tomography

Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation

Phase retrieval in in-line x-ray phase contrast imaging based on total variation minimization

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