Smoothed-NUV Priors for Imaging
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Date
2022
Publication Type
Journal Article
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yes
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Abstract
Variations of L1-regularization including, in particular, total variation regularization, have hugely improved computational imaging. However, sharper edges and fewer staircase artifacts can be achieved with convex-concave regularizers. We present a new class of such regularizers using normal priors with unknown variance (NUV), which include smoothed versions of the logarithm function and smoothed versions of L-p norms with p <= 1. All NUV priors allow variational representations that lead to efficient algorithms for image reconstruction by iterative reweighted descent. A preferred such algorithm is iterative reweighted coordinate descent, which has no parameters (in particular, no step size to control) and is empirically robust and efficient. The proposed priors and algorithms are demonstrated with applications to tomography. We also note that the proposed priors come with built-in edge detection, which is demonstrated by an application to image segmentation.
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published
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Volume
31
Pages / Article No.
4663 - 4678
Publisher
IEEE
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Subject
Image reconstruction; NUV; sparsity; iterative reweighted descent; expectation maximization; tomography; edge detection; image segmentation
Organisational unit
03568 - Loeliger, Hans-Andrea / Loeliger, Hans-Andrea
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