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Alan C. Bovik

Researcher at University of Texas at Austin

Publications -  872
Citations -  120104

Alan C. Bovik is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Image quality & Video quality. The author has an hindex of 102, co-authored 837 publications receiving 96088 citations. Previous affiliations of Alan C. Bovik include University of Illinois at Urbana–Champaign & University of Sydney.

Papers
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Journal ArticleDOI

Generalized Gaussian scale mixtures: A model for wavelet coefficients of natural images

TL;DR: It is shown that the GGSM model can lead to improved performance in distortion-related applications, while providing a more principled approach to the statistical processing of distorted image signals.
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Robust techniques for edge detection in multiplicative Weibull image noise

TL;DR: This paper defines and compares some novel techniques for the robust detection of sustained image irradiance changes, or edges, in images immersed in multiplicative Weibull noise, and suggests that edge detection using ratios of single order statistics (ROS detector) offers the best compromise among computational convenience, edge localization and robust performance.
Proceedings ArticleDOI

Spatio-Temporal Measures Of Naturalness

TL;DR: The spatiotemporal statistic of a wide variety of natural videos is studied, new directional temporal statistical models of videos are constructed, and whether measures of directional spatio-temporal naturalness can be developed that are predictive of quality are studied.
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Comparison of algorithms to enhance spicules of spiculated masses on mammography.

TL;DR: An algorithm for enhancement of spicules ofSpiculated masses, which uses the discrete radon transform, is developed and it is found that most observers preferred the enhanced images generated with the fast slant stack (FSS) method.
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Generalized deterministic annealing

TL;DR: The empirical data taken in conjunction with the formal analytical results suggest that GDA enjoys significant performance advantages relative to current methods for combinatorial optimization.