Research on Blind Deconvolution Algorithm of Multiframe Turbulence-degraded Images
Lijuan Zhang · Journal of Information and Computational Science · 2013
The restoration algorithm based on frame selection and multi-frame blind deconvolution is proposed in this paper for restoring object image from a sequence of turbulence-degraded images with noise. The algorithm has been applied to Maximum-likelihood estimates as the basic principles. The logarithmic Maximum-likelihood function of multi-frame images is built according to the image Gaussian noise models, and the iterative relationships to estimate the PSFs and object image are derived by maximizing the logarithmic Maximum-likelihood function. Meanwhile, the bandwidth limit function about the support of the PSFs is estimated in view of optical system parameters and Fourier optical theory, and incorporated into the iterative process of the restoration as that increase the convergence and stability of the Maximum-likelihood algorithm. In order to test the validity of the proposed algorithm, a series of restoration experiments are performed on the sequence turbulence-degraded images with completely unknown the degradation model and the experiments results show that the proposed algorithm can effectively restore the ground-in object from their turbulence-degraded images.