Enhanced Biggs---Andrews Asymmetric Iterative Blind Deconvolution
Multidimensional Systems and Signal Processing
A Bayesian super-resolution approach to demosaicing of blurred images
EURASIP Journal on Applied Signal Processing
Super-resolution using hidden Markov model and Bayesian detection estimation framework
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Advances in Signal Processing
Determining the regularization parameters for super-resolution problems
Signal Processing
A Simple Scaling Algorithm Based on Areas Pixels
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Estimation of the parameters in regularized simultaneous super-resolution
Pattern Recognition Letters
Adaptive multiple-frame image super-resolution based on U-curve
IEEE Transactions on Image Processing
Bayesian reconstruction of color images acquired with a single CCD
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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We consider the estimation of the unknown parameters for the problem of reconstructing a high-resolution image from multiple undersampled, shifted, degraded frames with subpixel displacement errors. We derive mathematical expressions for the iterative calculation of the maximum likelihood estimate of the unknown parameters given the low resolution observed images. These iterative procedures require the manipulation of block-semi circulant (BSC) matrices, that is, block matrices with circulant blocks. We show how these BSC matrices can be easily manipulated in order to calculate the unknown parameters. Finally the proposed method is tested on real and synthetic images.