Optimization of Initial-Value Choosing in the Maximum-Likelihood Blind Deconvolution with PSF Parameterized by Zernike Polynomials
Jiang Nan-liwe · Optics & Optoelectronic Technology · 2014
To improve the quality of the image restored by blind deconvolution,three initial-value-choosing methods are proposed,namely the experimental method,the Gaussian point spread function fitting method and the Kolmogorov spectrum method.Zernike polynomials are introduced to parameterize the point spread function,and the maximum-likelihood iterative blind deconvolution algorithm is applied to restore the blurred SEASAT image and the observed image of Jupiter.Experimental results show that the images have better details when restored by applying the Kolmogorove spectrum method and the Gaussian point spread function method.