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Variational models for multiplicative noise removal
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- Authors
- Advisor
- 강명주
- Major
- 자연과학대학 수리과학부
- Issue Date
- 2017-08
- Publisher
- 서울대학교 대학원
- Keywords
- image denoising ; multiplicative Gamma noise ; spatially adaptive regularization parameter ; proximal linearized alternating direction algorithm ; nonconvex total generalized variation ; iteratively reweighted `1 algorithm
- Description
- 학위논문 (박사)-- 서울대학교 대학원 자연과학대학 수리과학부, 2017. 8. 강명주.
- Abstract
- This dissertation discusses a variational partial differential equation (PDE) models for restoration of images corrupted by multiplicative Gamma noise. The two proposed models are suitable for heavy multiplicative noise which is often seen in applications. First, we propose a total variation (TV) based model with local constraints. The local constraint involves multiple local windows which is related a spatially adaptive regularization parameter (SARP). In addition, convergence analysis such as the existence and uniqueness of a solution is also provided. Second model is an extension of the first one using nonconvex version of the total generalized variation (TGV). The nonconvex TGV regularization enables to efficiently denoise smooth regions, without staircasing artifacts that appear on total variation regularization based models, and to conserve edges and details.
- Language
- English
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