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8 February 2005 A robust blind deconvolution based on estimation of point spread function parameters
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At present, in the field of image processing, the main algorithm to restore the blurred image is the blind deconvolution. But most of the blind deconvolution methods have to iterate a large amount of times and the result is also unsatisfactory. In this paper, a new blind deconvolution algorithm is proposed, which, consisting of two steps, is based on simultaneous estimating the specimen function and the parameters of the point-spread function (PSF). Firstly, it uses the expectation maximization algorithm (EM) to iterate the specimen function; secondly it uses the conjugate gradient method to estimate the parameters of the PSF. The mathematical model ensures that all the constraints of the PSF are satisfied, and the maximum-likelihood approach ensures that the specimen is nonnegative. In this paper, the general Gauss function is used to be as the PSF. In the experiment, it can successfully restore both the two-dimensional and three-dimensional images within limited times of iteration.
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Qingchuan Tao, Jianguo Chen, Qizhi Teng, Ying Liu, and Xiaohai He "A robust blind deconvolution based on estimation of point spread function parameters", Proc. SPIE 5637, Electronic Imaging and Multimedia Technology IV, (8 February 2005);

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