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    National Tsing Hua University Institutional Repository > 理學院 > 數學系 > 會議論文 >  A Method for Total Variation-based Reconstruction of Noisy and Blurred Images

    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/45440

    Title: A Method for Total Variation-based Reconstruction of Noisy and Blurred Images
    Authors: Qianshun Chang;Weicheng Wang;Jing Xu
    教師: 王偉成
    Date: 2006
    Publisher: Springer Link
    Relation: Image Processing Based on Partial Differential Equations,Springer Berlin Heidelberg,2006,Pages 95-108
    Keywords: Image restoration
    total variation
    nonlinear iteration
    algebraic multigrid method
    Krylov acceleration
    Abstract: In this paper, we focus on deblurring and denoising problems for blurred images with moderate or large noise. A new algorithm for the discretized system is presented.Convergence of outer iteration is efficiently improved by adding a linear term on both sides of the system of nonlinear equations. In inner iteration, an algebraic multigrid (AMG) method is applied to solve the linearized systems of equations. We adopt the Krylov subspace method to accelerate the outer nonlinear iteration. Numerical experiments demonstrate that this method is efficient and robust even for images with large noise-to-signal ratios and signal to blurring quantity ratios.
    URI: http://www.springerlink.com/
    Appears in Collections:[數學系] 會議論文

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