SHERP

Nonconvex TGV-Shearlet Based Model for Compressive Sensing
비볼록 일반적 총변이와 쉬어렛 변환을 이용한 압축센싱

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Authors
김정훈
Advisor
강명주
Major
자연과학대학 협동과정 계산과학전공
Issue Date
2018
Publisher
서울대학교 대학원
Keywords
Compressive SensingTotal generalized variationShearlet transformNonconvex regularizationIteratively reweighted l1 algorithmAlternating direction method of multipliers
Description
학위논문 (석사)-- 서울대학교 대학원 : 자연과학대학 협동과정 계산과학전공, 2018. 2. 강명주.
Abstract
Compressive sensing (CS), an area of signal processing, refers to the reconstruction of sparse or compressible signals from a fewer measurement. When we solve energy functional minimization problem for CS, the model consists of the data fidelity term and the regularization term. The regularization term is of importance as well as data fidelity term. In this thesis, we propose CS model by employing a hybrid of nonconvex total generalized variation (NTGV) and shearlet transform as a regularization tool. NTGV and shearlet transform behave complementary each other in CS problem as a regularization tool. Moreover, this thesis also proposes a numerical algorithm for proposed NTGV-Shearlet regularization based model by adopting iteratively reweighted L1 (IRL1) algorithm, one of the nonconvex optimization algorithms. Alternating direction method of multipliers, a well-known convex optimization problem, is also used to solve inner convex problem of our proposed model. We compare numerical results of our NTGV-Shearlet based CS model experiments to those of three other closely related state-of-the-art regularization tool based CS models experiments. Numerical results show that our NTGV-Shearlet based CS model outperforms other three CS models both visually and in terms of peak signal-to-noise ratio (PSNR).
Language
English
URI
http://hdl.handle.net/10371/142482
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College of Natural Sciences (자연과학대학)Program in Computational Science and Technology (협동과정-계산과학전공)Theses (Master's Degree_협동과정-계산과학전공)
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