ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Overview of total least-squares methods
Signal Processing
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Advances in Signal Processing
International Journal of Computational Science and Engineering
Multiframe image restoration in the presence of noisy blur kernel
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Structured least squares problems and robust estimators
IEEE Transactions on Signal Processing
A qualitative-quantitative comparison of image motion deblurring algorithms
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Fast deconvolution with approximated PSF by RSTLS with antireflective boundary conditions
Journal of Computational and Applied Mathematics
Variational multiframe restoration of images degraded by noisy (stochastic) blur kernels
Journal of Computational and Applied Mathematics
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In this paper, the problem of restoring an image distorted by a linear space-invariant (LSI) point-spread function (PSF) that is not exactly known is formulated as the solution of a perturbed set of linear equations. The regularized constrained total least-squares (RCTLS) method is used to solve this set of equations. Using the diagonalization properties of the discrete Fourier transform (DFT) for circulant matrices, the RCTLS estimate is computed in the DFT domain. This significantly reduces the computational cost of this approach and makes its implementation possible even for large images. An error analysis of the RCTLS estimate, based on the mean-squared-error (MSE) criterion, is performed to verify its superiority over the constrained total least-squares (CTLS) estimate. Numerical experiments for different errors in the PSF are performed to test the RCTLS estimator. Objective and visual comparisons are presented with the linear minimum mean-squared-error (LMMSE) and the regularized least-squares (RLS) estimator. Our experiments show that the RCTLS estimator reduces significantly ringing artifacts around edges as compared to the two other approaches