Variational models for multiplicative noise removal

학위논문 (박사)-- 서울대학교 대학원 자연과학대학 수리과학부, 2017. 8. 강명주.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.1. Introduction 1 2. Previous works 6 2.1 Variational models for image denoising 6 2.2.1 Convex and nonconvex regularizers 6 2.2.2 Variational models for multiplicative noise removal 8 2.2 Proximal linearized alternating direction method of multipliers 10 3. Proposed models 13 3.1 Proposed model 1 :exp TV model with SARP 13 3.1.1 Derivation of our model 13 3.1.2 Proposed TV model with local constraints 16 3.1.3 A SARP algorithm for solving model (3.1.16) 27 3.1.4 Numerical results 32 3.2 Proposed model 2 :exp NTGV model with SARP 51 3.2.1 Proposed NTGV model 51 3.2.2 Updating rule for $\lambda(x)$ in (3.2.1) 52 3.2.3 Algorithm for solving the proposed model (3.2.1) 55 3.2.4 Numerical results 62 3.2.5 Selection of parameters 63 3.2.6 Image denoising 65 4. Conclusion 79Docto

Diffusion-Steered Super-Resolution Image Reconstruction

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