Iterative methods for image deblurring: A Matlab object-oriented approach In iterative image restoration methods, implementation of efficient matrix vector multiplication, and linear system solves for preconditioners, can be a tedious and time consuming process. Different blurring functions and boundary conditions often require implementing different data structures and algorithms. A complex set of computational methods is needed, each likely having different input parameters and calling sequences. This paper describes a set of Matlab tools that hide these complicated implementation details. Combining the powerful scientific computing and graphics capabilities in Matlab, with the ability to do object-oriented programming and operator overloading, results in a set of classes that is easy to use, and easily extensible.

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  1. Cai, Yuantao; Donatelli, Marco; Bianchi, Davide; Huang, Ting-Zhu: Regularization preconditioners for frame-based image deblurring with reduced boundary artifacts (2016)
  2. De Asmundis, Roberta; di Serafino, Daniela; Landi, Germana: On the regularizing behavior of the SDA and SDC gradient methods in the solution of linear ill-posed problems (2016)
  3. Bakushinsky, Anatoly; Smirnova, Alexandra; Liu, Hui: A nonstandard approximation of pseudoinverse and a new stopping criterion for iterative regularization (2015)
  4. Donatelli, Marco; Martin, David; Reichel, Lothar: Arnoldi methods for image deblurring with anti-reflective boundary conditions (2015)
  5. Jiang, Le; Huang, Jin; Lv, Xiao-Guang; Liu, Jun: Alternating direction method for the high-order total variation-based Poisson noise removal problem (2015)
  6. Kernfeld, Eric; Kilmer, Misha; Aeron, Shuchin: Tensor-tensor products with invertible linear transforms (2015)
  7. Lv, Xiao-Guang; Song, Yong-Zhong; Li, Fang: An efficient nonconvex regularization for wavelet frame and total variation based image restoration (2015)
  8. Huang, Jie; Huang, Ting-Zhu; Zhao, Xi-Le; Xu, Zong-Ben; Lv, Xiao-Guang: Two soft-thresholding based iterative algorithms for image deblurring (2014)
  9. Danelakis, Antonios; Mitrouli, Marilena; Triantafyllou, Dimitrios: Blind image deconvolution using a banded matrix method (2013)
  10. Jiang, Le; Huang, Jin; Lv, Xiao-Guang; Liu, Jun: Restoring Poissonian images by a combined first-order and second-order variation approach (2013)
  11. Landi, Germana; Piccolomini, Elena Loli: NPtool: a MATLAB software for nonnegative image restoration with Newton projection methods (2013)
  12. Donatelli, Marco: On nondecreasing sequences of regularization parameters for nonstationary iterated Tikhonov (2012)
  13. Lv, Xiao-Guang; Huang, Ting-Zhu; Xu, Zong-Ben; Zhao, Xi-Le: Kronecker product approximations for image restoration with whole-sample symmetric boundary conditions (2012)
  14. Redivo-Zaglia, Michela; Rodriguez, Giuseppe: smt: A Matlab toolbox for structured matrices (2012)
  15. Zhao, Xi-Le; Huang, Ting-Zhu; Lv, Xiao-Guang; Xu, Zong-Ben; Huang, Jie: Kronecker product approximations for image restoration with new mean boundary conditions (2012)
  16. Fan, Ying Wai (Daniel); Nagy, James G.: Synthetic boundary conditions for image deblurring (2011)
  17. Chung, Julianne; Nagy, James G.: An efficient iterative approach for large-scale separable nonlinear inverse problems (2010)
  18. Donatelli, Marco; Serra-Capizzano, Stefano: Antireflective boundary conditions for deblurring problems (2010)
  19. Golub, Gene H.; Meurant, GĂ©rard: Matrices, moments and quadrature with applications (2010)
  20. Huckle, T.; Sedlacek, M.: Smoothing and regularization with modified sparse approximate inverses (2010)

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