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10 July 2008 An iterative deconvolution algorithm using combined regularization for low-order corrected astronomical images
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Abstract
An iterative deconvolution algorithm is presented in detail which utilizes regularization to combine maximum-likelihood (ML) estimate of convolution error and several physical constraints to build error function. The physical constraints used in this algorithm include positivity, band-limit information and the information of multiple frames. By minimizing the combined error metric of individual ones, the object can be expected to be recovered from the noisy data. In addition, numerical simulation of Phase Screen distorted by atmospheric turbulence following the Kolmogorov spectrum is also made to generate the PSFs which are used to simulate the degraded images.
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Hualin Chen, Xiangyan Yuan, and Xiangqun Cui "An iterative deconvolution algorithm using combined regularization for low-order corrected astronomical images", Proc. SPIE 7015, Adaptive Optics Systems, 70152F (10 July 2008); https://doi.org/10.1117/12.787564
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