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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Patent
Method and system for object reconstruction
TL;DR: In this article, a system consisting of an illuminating unit and an imaging unit is presented for real-time reconstruction of a three-dimensional map of the object in the optical path of illuminating light propagating from the light source towards an object, thereby projecting onto the object a coherent random speckle pattern.
Journal ArticleDOI
Richardson-Lucy Deblurring for Scenes under a Projective Motion Path
TL;DR: This paper discusses how the blurred image can be modeled as an integration of the clear scene under a sequence of planar projective transformations that describe the camera's path, and describes how to modify the Richardson-Lucy algorithm to incorporate this new blur model.
Posted ContentDOI
Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy
Martin Weigert,Uwe Schmidt,Tobias Boothe,Andreas Müller,Alexandr Dibrov,Akanksha Jain,Benjamin Wilhelm,Deborah Schmidt,Coleman Broaddus,Siân Culley,Mauricio Rocha-Martins,Fabián Segovia-Miranda,Caren Norden,Ricardo Henriques,Marino Zerial,Michele Solimena,Jochen C. Rink,Pavel Tomancak,Loic Royer,Florian Jug,Eugene W. Myers,Eugene W. Myers +21 more
TL;DR: This work shows how deep learning enables biological observations beyond the physical limitations of microscopes, and illustrates how microscopy images can be restored even if 60-fold fewer photons are used during acquisition.
Journal ArticleDOI
A scaled gradient projection method for constrained image deblurring
TL;DR: In this paper, a special gradient projection method is introduced that exploits effective scaling strategies and steplength updating rules, appropriately designed for improving the convergence rate, and the authors give convergence results for this scheme and evaluate its effectiveness by means of an extensive computational study on the minimization problems arising from the maximum likelihood approach to image deblurring.
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.