Digital Image Restoration
Tomographic Reconstruction Using Information-Weighted Spline Smoothing
IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging
Regularized constrained total least squares image restoration
IEEE Transactions on Image Processing
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An iterative maximum a posteriori (MAP) algorithm is proposed for simultaneous signal-covariance estimation and restoration when only partial knowledge of the system response matrix (SRM) and the noisy-blurred sinogram of an image to be reconstructed are available. Convergence analysis is performed to ascertain that the proposed covariance estimator converges to the optimal one in the MAP sense. The superiority of the proposed algorithm, in comparison with the iterative linear minimum mean-squared-error (LMMSE) filter for incorrect SRM information, is experimentally verified.