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Restoration algorithms for turbulence-degraded images based on optimized estimation of discrete values of overall point spread functions

机译：Restoration algorithms for turbulence-degraded images based on optimized estimation of discrete values of overall point spread functions

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摘要

For the problem of restoration of turbulence-degraded images, it is of utmost importance to make a correct estimation of the turbulence’ s stochastic point spread function (PSF). A new method is presented for estimating the discrete values of overall PSFs of turbulence-degraded images. For this method, two short-exposure turbulence-degraded images are used as the inputs, for which the Fourier transforms are made and a series of equations for calculating the discrete values of the turbulence PSFs are developed. Some effective rules for selecting equations have been worked out to ensure a reliable solution for the PSFs. To overcome the interference of noise, two optimization algorithms for estimating the turbulence PSF values, based on quadratic and nonquadratic regularization that can be incorporated into the estimation process, are proposed, in which the constraints of the PSF values are non-negative and smooth [quadratic regularization non- negative and smooth (QRNNS) and nonquadratic regularization non- negative and smooth (NQRNNS)]. A series of experiments are per- formed to test the algorithms proposed, which show that the NQRNNS algorithm is both rational and highly effective

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