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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.
Journal ArticleDOI
Robust techniques for edge detection in multiplicative Weibull image noise
Robin A. Brooks,Alan C. Bovik +1 more
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
Zeina Sinno,Alan C. Bovik +1 more
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.
Journal ArticleDOI
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.
Journal ArticleDOI
Generalized deterministic annealing
Scott T. Acton,Alan C. Bovik +1 more
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.