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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2017.2685342
|2 doi
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|a eng
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| 100 |
1 |
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|a Ravazzi, Chiara
|e verfasserin
|4 aut
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| 245 |
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|a Curl-Constrained Gradient Estimation for Image Recovery From Highly Incomplete Spectral Data
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| 264 |
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|c 2017
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 30.07.2018
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|a Date Revised 30.07.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a In this paper, we introduce new gradient-based methods for image recovery from a small collection of spectral coefficients of the Fourier transform, which is of particular interest for several scanning technologies, such as magnetic resonance imaging. Since gradients of a medical image are much more sparse or compressible than the corresponding image, classical l1 -minimization methods have been used to recover these relative differences. The image values can then be obtained by integration algorithms imposing boundary constraints. Compared with classical gradient recovery methods, we propose two new techniques that improve reconstruction. First, we cast the gradient recovery problem as a compressed sensing problem taking into account that the curl of the gradient field should be zero. Second, inspired by the emerging field of signal processing on graphs, we formulate the gradient recovery problem as an inverse problem on graphs. Iteratively reweighted l1 recovery methods are proposed to recover these relative differences and the structure of the similarity graph. Once the gradient field is estimated, the image is recovered from the compressed Fourier measurements using least squares estimation. Numerical experiments show that the proposed approach outperforms the state-of-the-art image recovery methods
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|a Journal Article
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|a Coluccia, Giulio
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Magli, Enrico
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 26(2017), 6 vom: 16. Juni, Seite 2656-2668
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|x 1941-0042
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|g volume:26
|g year:2017
|g number:6
|g day:16
|g month:06
|g pages:2656-2668
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|u http://dx.doi.org/10.1109/TIP.2017.2685342
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