RestoreTools

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.


References in zbMATH (referenced in 47 articles )

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  1. Aminikhah, Hossein; Yousefi, Mahsa: A special generalized HSS method for discrete ill-posed problems (2018)
  2. Hnětynková, Iveta; Kubínová, Marie; Plešinger, Martin: Noise representation in residuals of LSQR, LSMR, and CRAIG regularization (2017)
  3. Renaut, Rosemary A.; Vatankhah, Saeed; Ardestani, Vahid E.: Hybrid and iteratively reweighted regularization by unbiased predictive risk and weighted GCV for projected systems (2017)
  4. Cai, Yuantao; Donatelli, Marco; Bianchi, Davide; Huang, Ting-Zhu: Regularization preconditioners for frame-based image deblurring with reduced boundary artifacts (2016)
  5. 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)
  6. Donatelli, Marco; Huckle, Thomas; Mazza, Mariarosa; Sesana, Debora: Image deblurring by sparsity constraint on the Fourier coefficients (2016)
  7. Gazzola, Silvia; Reichel, Lothar: A new framework for multi-parameter regularization (2016)
  8. Bakushinsky, Anatoly; Smirnova, Alexandra; Liu, Hui: A nonstandard approximation of pseudoinverse and a new stopping criterion for iterative regularization (2015)
  9. Donatelli, Marco; Martin, David; Reichel, Lothar: Arnoldi methods for image deblurring with anti-reflective boundary conditions (2015)
  10. Jiang, Le; Huang, Jin; Lv, Xiao-Guang; Liu, Jun: Alternating direction method for the high-order total variation-based Poisson noise removal problem (2015)
  11. Kernfeld, Eric; Kilmer, Misha; Aeron, Shuchin: Tensor-tensor products with invertible linear transforms (2015)
  12. Lv, Xiao-Guang; Song, Yong-Zhong; Li, Fang: An efficient nonconvex regularization for wavelet frame and total variation based image restoration (2015)
  13. Huang, Jie; Huang, Ting-Zhu; Zhao, Xi-Le; Xu, Zong-Ben; Lv, Xiao-Guang: Two soft-thresholding based iterative algorithms for image deblurring (2014) ioport
  14. Danelakis, Antonios; Mitrouli, Marilena; Triantafyllou, Dimitrios: Blind image deconvolution using a banded matrix method (2013)
  15. Jiang, Le; Huang, Jin; Lv, Xiao-Guang; Liu, Jun: Restoring Poissonian images by a combined first-order and second-order variation approach (2013)
  16. Landi, Germana; Piccolomini, Elena Loli: NPtool: a MATLAB software for nonnegative image restoration with Newton projection methods (2013)
  17. Donatelli, Marco: On nondecreasing sequences of regularization parameters for nonstationary iterated Tikhonov (2012)
  18. 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)
  19. Redivo-Zaglia, Michela; Rodriguez, Giuseppe: smt: A Matlab toolbox for structured matrices (2012)
  20. 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)

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