Smoothed-NUV Priors for Imaging and Beyond

Boxiao Ma

$142.95 $114.40

Paperback

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English

Hartung & Gorre
21 April 2022

Applied optics

Summary
Details

Many problems in imaging need to be guided with effective priors or reg- ularizations for different reasons. A great variety of regularizations have been proposed that have substantially improved computational imaging and driven the area to a whole new level. The most famous and widely applied among them is L1-regularization and its variations, including total variation (TV) regularization in particular. This thesis presents an alternative class of regularizations for imaging using normal priors with unknown variance (NUV), which produce sharp edges and few staircase artifacts. While many regularizations (includ- ing TV) prefer piecewise constant images, which leads to staricasing, the smoothed-NUV (SNUV) priors have a convex-concave structure and thus prefer piecewise smooth images. We argue that piecewise smooth is a more realistic assumption compared to piecewise constant and is crucial for good imaging results. The thesis is organized in three parts.

By: Boxiao Ma
Imprint: Hartung & Gorre
Dimensions: Height: 210mm, Width: 148mm, Spine: 10mm
Weight: 218g
ISBN: 9783866287464
ISBN 10: 3866287461
Pages: 178
Publication Date: 21 April 2022
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Active

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Smoothed-NUV Priors for Imaging and Beyond

Boxiao Ma

$142.95 $114.40

Paperback

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