Maximum likelihood blind image restoration via alternating minimization
A new algorithm for Maximum likelihood blind image restoration is presented in this paper. It is obtained by modeling the original image and the additive noise as multivariate Gaussian processes with unknown covariance matrices. The blurring process is specified by its point spread function, which is also unknown. Estimations of the original image and the blur are derived by alternating minimization of the Kullback-Leibler divergence. The algorithm presents the advantage to provide closed form...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of IEEE International Conference on Image Processing 2010|
|01_Seghouane_Maximum_likelihood_blind_image_2010.pdf||269.04 kB||Adobe PDF||Request a copy|
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