Abstract
In order to eliminate the influence of atmospheric turbulence on astronomic observation and recognizing targets, a blind deconvolution image restoration algorithm based on maximum Likelihood is proposed. The basic principle of the algorithm is maximum likelihood theory. It transforms image restoration into the minimization of a penalizing function, and the course of minimization of the penalizing function is just the one of restored image approaching real result. The paper calculates the minimum of penalizing function by use of conjugate gradient method, and fast implements the method through across iterativeness of the two convolution component (restored image and PSF). The experiment of restoring simulative turbulence-degraded image and real turbulence-degraded image shows when the model of turbulence-degraded is entirely unknown the algorithm can realize the reconstruction of degraded image, and is capable of resisting-noise with robustness.
Published Version
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