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
Drizzle: A Method for the Linear Reconstruction of Undersampled Images
Andrew S. Fruchter,Richard Hook +1 more
TL;DR: In this article, a variable-pixel linear reconstruction (VPLR) method was proposed for linear reconstruction of an image from under-sampled, dithered data, which preserves photometry and resolution, weight input images according to the statistical significance of each pixel, and removes the effects of geometric distortion both on image shape and photometry.
Proceedings Article
Deep Convolutional Neural Network for Image Deconvolution
Li Xu,Jimmy Ren,Ce Liu,Jiaya Jia +3 more
TL;DR: This work develops a deep convolutional neural network to capture the characteristics of degradation, establishing the connection between traditional optimization-based schemes and a neural network architecture where a novel, separable structure is introduced as a reliable support for robust deconvolution against artifacts.
Journal ArticleDOI
Algorithms for nonnegative matrix factorization with the β-divergence
Cédric Févotte,Jérôme Idier +1 more
TL;DR: This letter describes algorithms for nonnegative matrix factorization (NMF) with the β-divergence, a family of cost functions parameterized by a single shape parameter β that takes the Euclidean distance, the Kullback-Leibler divergence, and the Itakura-Saito divergence as special cases.
Journal ArticleDOI
Breaking the diffraction barrier in fluorescence microscopy at low light intensities by using reversibly photoswitchable proteins
TL;DR: The surpassing of the diffraction barrier in fluorescence microscopy with illumination intensities that are eight orders of magnitude smaller is demonstrated, underscoring the potential to finally achieve molecular resolution in fluorescent microscopy by technical optimization.
Journal ArticleDOI
Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images*
Stuart Geman,Donald Geman +1 more
TL;DR: In this paper, the authors make an analogy between images and statistical mechanics systems, where pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system.
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.