A Truncated Lagrange Method for Total Variation-Based Image Restoration
Journal of Mathematical Imaging and Vision
Iterative desensitisation of image restoration filters under wrong PSF and noise estimates
EURASIP Journal on Applied Signal Processing
High performance edge-preserving regularization in 3D SPECT imaging
Parallel Computing
The Lagrange method for the regularization of discrete ill-posed problems
Computational Optimization and Applications - Special issue: Numerical analysis of optimization in partial differential equations
The Lagrange method for the regularization of discrete ill-posed problems
Computational Optimization and Applications
Deblurring with rank-structured inverse approximations
SIGGRAPH '09: Posters
Optimal estimation of deterioration from diagnostic image sequence
IEEE Transactions on Signal Processing
An efficient parallel implementation of the MSPAI preconditioner
Parallel Computing
Smoothing and regularization with modified sparse approximate inverses
Journal on Image and Video Processing - Special issue on iterative signal processing in communications
Matrix Structures and Parallel Algorithms for Image Superresolution Reconstruction
SIAM Journal on Matrix Analysis and Applications
A hybrid multilevel-active set method for large box-constrained linear discrete ill-posed problems
Calcolo: a quarterly on numerical analysis and theory of computation
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Removing a linear shift-invariant blur from a signal or image can be accomplished by inverse or Wiener filtering, or by an iterative least-squares deblurring procedure. Because of the ill-posed characteristics of the deconvolution problem, in the presence of noise, filtering methods often yield poor results. On the other hand, iterative methods often suffer from slow convergence at high spatial frequencies. This paper concerns solving deconvolution problems for atmospherically blurred images by the preconditioned conjugate gradient algorithm, where a new approximate inverse preconditioner is used to increase the rate of convergence. Theoretical results are established to show that fast convergence can be expected, and test results are reported for a ground-based astronomical imaging problem