Journal ArticleDOI
Bayesian-Based Iterative Method of Image Restoration
TLDR
An iterative method of restoring degraded images was developed by treating images, point spread functions, and degraded images as probability-frequency functions and by applying Bayes’s theorem.Abstract:
An iterative method of restoring degraded images was developed by treating images, point spread functions, and degraded images as probability-frequency functions and by applying Bayes’s theorem. The method functions effectively in the presence of noise and is adaptable to computer operation.read more
Citations
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Alternating direction method for the high-order total variation-based Poisson noise removal problem
TL;DR: A high-order total variation-based optimization model is considered to remedy the shortcoming of TV in Poissonian image restoration and results from the Poisson noise removal problem are given to illustrate the validity and efficiency of the proposed method.
Journal ArticleDOI
A method to deconvolve stellar rotational velocities
TL;DR: In this article, the authors developed a method to deconvolve this inverse problem and obtain the cumulative distribution function for stellar rotational velocities extending the work of Chandrasekhar & Munch (1950, ApJ, 111, 142).
Journal ArticleDOI
Toward Precision Black Hole Masses with ALMA: NGC 1332 as a Case Study in Molecular Disk Dynamics
Aaron J. Barth,Jeremy Darling,Andrew J. Baker,Benjamin D. Boizelle,David A. Buote,Luis C. Ho,Jonelle L. Walsh +6 more
TL;DR: In this paper, the authors present results from a program of Atacama Large Millimeter/submillimeter Array (ALMA) CO(2-1) observations of circumnuclear gas disks in early-type galaxies.
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Analysis of an approximate model for Poisson data reconstruction and a related discrepancy principle
TL;DR: In this article, an approximate model for Poisson data reconstruction inspired by a discrepancy principle for the selection of the regularization parameter, recently proposed by Bardsley and Goldes, was investigated.
Journal ArticleDOI
Restoration of interferometric images. III. Efficient Richardson-Lucy methods for LINC-NIRVANA data reduction
TL;DR: New techniques for accelerating the Ordered Subsets - Expectation Maximization (OS-EM) method are proposed and approaches based on the fusion of the multiple images into a single one are analyzed, so that one can use single-image deconvolution methods which are presumably more efficient than the multiple-image ones.
References
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Book
Modern probability theory and its applications
TL;DR: Probability Theory as the study of Mathematical Models of Random Phenomena as mentioned in this paper is a generalization of probability theory for the study and analysis of statistical models of random variables.
Journal ArticleDOI
Image Evaluation and Restoration
TL;DR: The extent to which the processing approaches the optimum can be evaluated by determining the fraction of the total information content of the image which can be visually extracted after processing.
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Restoration of Turbulence-Degraded Images*
TL;DR: In this paper, the amplitude and phase coefficients of the two-dimensional Fourier series representing the degraded images were corrected by applying corrections to the optical transfer function of the turbulence measured at the time the images were photographed.