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

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 performed to test the algorithms proposed, which show that the NQRNNS algorithm is both rational and highly effective.

[1]  B. Roy Frieden.  Turbulent image reconstruction using object power spectrum information , 1994 .

[2]  Naoshi Baba,et al.  Extended-object reconstruction with sequential use of the iterative blind deconvolution method , 1992 .

[3]  Yuanmei Wang,et al.  Multicriteria neural network approach to turbulent image reconstruction , 1997 .

[4]  Lam,et al.  Iterative statistical approach to blind image deconvolution , 2000, Journal of the Optical Society of America. A, Optics, image science, and vision.

[5]  James G. Nagy,et al.  Iterative image restoration using approximate inverse preconditioning , 1996, IEEE Trans. Image Process..

[6]  Nikolas P. Galatsanos,et al.  Multichannel restoration of single channel images using a wavelet-based subband decomposition , 1994, IEEE Trans. Image Process..

[7]  B. L. Ellerbroek,et al.  Adaptive wavefront control algorithms for closed loop adaptive optics , 2001 .

[8]  B. R. Frieden,et al.  Turbulent image reconstruction from a superposition model , 1993 .

[9]  A. Labeyrie.  Attainment of diffraction limited resolution in large telescopes by Fourier analysing speckle patterns in star images , 1970 .

[10]  Michael C. Roggemann,et al.  Constrained least-squares estimation in deconvolution from wave-front sensing , 1998 .

[11]  H. Malcolm Hudson,et al.  Maximum likelihood restoration and choice of smoothing parameter in deconvolution of image data subject to Poisson noise , 1998 .

[12]  Ngai-Fong Law,et al.  Blind deconvolution using least squares minimisation , 1996 .

[13]  Michel Barlaud,et al.  Deterministic edge-preserving regularization in computed imaging , 1997, IEEE Trans. Image Process..

[14]  L. Trefethen,et al.  Numerical linear algebra , 1997 .

[15]  Aggelos K. Katsaggelos,et al.  General choice of the regularization functional in regularized image restoration , 1995, IEEE Trans. Image Process..

[16]  B. Roy Frieden,et al.  Probability, Statistical Optics, And Data Testing , 1982 .

[17]  K. Knox,et al.  Recovery of Images from Atmospherically Degraded Short-Exposure Photographs , 1974 .

[18]  J. C. Fontanella,et al.  Reconstruction of turbulence-degraded images using the Knox–Thompson algorithm , 1987 .

[19]  Donald Geman,et al.  Constrained Restoration and the Recovery of Discontinuities , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Richard G. Lane,et al.  Blind deconvolution of speckle images , 1992 .

[21]  James G. Nagy,et al.  Space-varying restoration of optical images , 1997 .

[22]  J. C. Dainty,et al.  Iterative blind deconvolution method and its applications , 1988 .

[23]  B. R. Frieden.  An exact, linear solution to the problem of imaging through turbulence , 1998 .

[24]  Timothy J. Schulz,et al.  Multiframe blind deconvolution of astronomical images , 1993 .

[25]  B. Welsh,et al.  Imaging Through Turbulence , 1996 .

[26]  A. Lohmann,et al.  Speckle masking in astronomy: triple correlation theory and applications. , 1983, Applied optics.

[27]  Yoram Bresler,et al.  Globally convergent edge-preserving regularized reconstruction: an application to limited-angle tomography , 1998, IEEE Trans. Image Process..

[28]  Charles L. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[29]  Donald Geman,et al.  Nonlinear image recovery with half-quadratic regularization , 1995, IEEE Trans. Image Process..

[30]  Andrew J. Lambert,et al.  Anisoplanatic Image Restoration at ADFA , 2003, DICTA.