ui.adsabs.harvard.edu/abs/1999ITIP....8..286C/abstract

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https://ui.adsabs.harvard.edu/abs/1999ITIP....8..286C/abstract

Cross Burg entropy maximization and its application to ringing suppression in image reconstruction.

The authors present a multiplicative algorithm for image reconstruction, together with a partial convergence proof. The iterative scheme aims to maximize cross Burg entropy between modeled and measured data. Its application to infrared astronomical satellite (IRAS) data shows reduced ringing around point sources, compared to the EM (Richardson-Lucy) algorithm.



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Cross Burg entropy maximization and its application to ringing suppression in image reconstruction.

https://ui.adsabs.harvard.edu/abs/1999ITIP....8..286C/abstract

The authors present a multiplicative algorithm for image reconstruction, together with a partial convergence proof. The iterative scheme aims to maximize cross Burg entropy between modeled and measured data. Its application to infrared astronomical satellite (IRAS) data shows reduced ringing around point sources, compared to the EM (Richardson-Lucy) algorithm.



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https://ui.adsabs.harvard.edu/abs/1999ITIP....8..286C/abstract

Cross Burg entropy maximization and its application to ringing suppression in image reconstruction.

The authors present a multiplicative algorithm for image reconstruction, together with a partial convergence proof. The iterative scheme aims to maximize cross Burg entropy between modeled and measured data. Its application to infrared astronomical satellite (IRAS) data shows reduced ringing around point sources, compared to the EM (Richardson-Lucy) algorithm.

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      The authors present a multiplicative algorithm for image reconstruction, together with a partial convergence proof. The iterative scheme aims to maximize cross Burg entropy between modeled and measured data. Its application to infrared astronomical satellite (IRAS) data shows reduced ringing around point sources, compared to the EM (Richardson-Lucy) algorithm.
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