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Proceedings Paper

Automatic parameter prediction for image denoising algorithms using perceptual quality features
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Paper Abstract

A natural scene statistics (NSS) based blind image denoising approach is proposed, where denoising is performed without knowledge of the noise variance present in the image. We show how such a parameter estimation can be used to perform blind denoising by combining blind parameter estimation with a state-of-the-art denoising algorithm.1 Our experiments show that for all noise variances simulated on a varied image content, our approach is almost always statistically superior to the reference BM3D implementation in terms of perceived visual quality at the 95% confidence level.

Paper Details

Date Published: 17 February 2012
PDF: 7 pages
Proc. SPIE 8291, Human Vision and Electronic Imaging XVII, 82910G (17 February 2012); doi: 10.1117/12.912243
Show Author Affiliations
Anish Mittal, The Univ. of Texas at Austin (United States)
Anush K. Moorthy, The Univ. of Texas at Austin (United States)
Alan C. Bovik, The Univ. of Texas at Austin (United States)

Published in SPIE Proceedings Vol. 8291:
Human Vision and Electronic Imaging XVII
Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Huib de Ridder, Editor(s)

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