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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Journal ArticleDOI
DeconvolutionLab2: An open-source software for deconvolution microscopy.
Daniel Sage,Laurène Donati,Ferréol Soulez,Denis Fortun,Guillaume Schmit,Arne Seitz,Romain Guiet,Cedric Vonesch,Michael Unser +8 more
TL;DR: This paper examines several standard algorithms used in deconvolution microscopy, notably: Regularized inverse filter, Tikhonov regularization, Landweber, T Sheikhonov-Miller, Richardson-Lucy, and fast iterative shrinkage-thresholding and distinguishes the algorithms in terms of image quality, performance, usability and computational requirements.
Journal ArticleDOI
Iterative reconstruction techniques in emission computed tomography.
Jinyi Qi,Richard M. Leahy +1 more
TL;DR: A review of recent progress in developing statistically based iterative techniques for emission computed tomography describes the different formulations of the emission image reconstruction problem and their properties and describes the numerical algorithms used for optimizing these functions.
Book ChapterDOI
Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database
TL;DR: This paper presents a benchmark dataset for motion deblurring that allows quantitative performance evaluation and comparison of recent approaches featuring non-uniform blur models, and evaluates state-of-the-art single image BD algorithms incorporating uniform and non- uniform blur Models.
Journal ArticleDOI
Noise properties of the EM algorithm: I. Theory.
TL;DR: The theory of expectation-maximization can be used as a basis for calculation of objective figures of merit for image quality over a wide range of conditions in emission tomography.
Book ChapterDOI
Single image deblurring using motion density functions
TL;DR: A novel single image deblurring method to estimate spatially non-uniform blur that results from camera shake that out-performs current approaches which make the assumption of spatially invariant blur.
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.
Journal ArticleDOI
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.