Blind image deblurring driven by nonlinear processing in the edge domain
EURASIP Journal on Applied Signal Processing
Stochastic super-resolution image reconstruction
Journal of Visual Communication and Image Representation
A closed form algorithm for superresolution
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Hierarchical blur identification from severely out-of-focus images
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
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We examine the restoration problem when the point-spread function (PSF) of the degradation system is partially known. For this problem, the PSF is assumed to be the sum of a known deterministic and an unknown random component. This problem has been examined before; however, in most previous works the problem of estimating the parameters that define the restoration filters was not addressed. In this paper, two iterative algorithms that simultaneously restore the image and estimate the parameters of the restoration filter are proposed using evidence analysis (EA) within the hierarchical Bayesian framework. We show that the restoration step of the first of these algorithms is in effect almost identical to the regularized constrained total least-squares (RCTLS) filter, while the restoration step of the second is identical to the linear minimum mean square-error (LMMSE) filter for this problem. Therefore, in this paper we provide a solution to the parameter estimation problem of the RCTLS filter. We further provide an alternative approach to the expectation-maximization (EM) framework to derive a parameter estimation algorithm for the LMMSE filter. These iterative algorithms are derived in the discrete Fourier transform (DFT) domain; therefore, they are computationally efficient even for large images. Numerical experiments are presented that test and compare the proposed algorithms