A General Framework for Nonlinear Regularized Krylov-Based Image Restoration

  • Serena Morigi
  • Lothar Reichel
  • Fiorella Sgallari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8641)


This paper introduces a new approach to computing an approximate solution of Tikhonov-regularized large-scale ill-posed problems with a general nonlinear regularization operator. The iterative method applies a sequence of projections onto generalized Krylov subspaces using a semi-implicit approach to deal with the nonlinearity in the regularization term. A suitable value of the regularization parameter is determined by the discrepancy principle. Computed examples illustrate the performance of the method applied to the restoration of blurred and noisy images.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Serena Morigi
    • 1
  • Lothar Reichel
    • 2
  • Fiorella Sgallari
    • 1
  1. 1.Department of MathematicsUniversity of BolognaBolognaItaly
  2. 2.Department of Mathematical SciencesKent State UniversityKentUSA

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