Smoothed-NUV Priors for Imaging and Beyond - Ma, Boxiao
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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…mehr

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Produktbeschreibung
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.
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Autorenporträt
Boxiao Ma was born in Wuhe, Anhui, China in 1994. He graduated from Bengbu No. 2 High School, China in 2009 and subse- quently joined the Chien-Shiung Wu College, Southeast University (SEU). He studied electrical engineering at Southeast University from 2009 to 2013 and obtained the Bachelor of Engineering (B. Eng.) degree. In 2013, he joined Technical University of Munich (TUM) to continue his study in electrical engineering and received the Master of Science (M. Sc.) degree in 2016. Since 2016, he has been a PhD Candidate and a full time scien- tific employee at Signal and Information Processing Laboratory at Swiss Federal Institute of Technology in Zurich (ETHZ). His research focuses on theory and applications of imaging priors. Concerning his working experience, he did an internship for 3 months at the ZTE Corporation in Nanjing, China in 2012. He worked as research assistant for almost 2 years from 2014 to 2016 at Highest Frequency Engi- neering Laboratory, TUM, and for 6 months from 2015 to 2016 at German Aerospace Center (DLR) in Oberpfaffenhofen, Germany.