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Proceedings Paper

Non-stationary noise estimation using dictionary learning and Gaussian mixture models
Author(s): James M. Hughes; Daniel N. Rockmore; Yang Wang
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Paper Abstract

Stationarity of the noise distribution is a common assumption in image processing. This assumption greatly simplifies denoising estimators and other model parameters and consequently assuming stationarity is often a matter of convenience rather than an accurate model of noise characteristics. The problematic nature of this assumption is exacerbated in real-world contexts, where noise is often highly non-stationary and can possess time- and space-varying characteristics. Regardless of model complexity, estimating the parameters of noise dis- tributions in digital images is a difficult task, and estimates are often based on heuristic assumptions. Recently, sparse Bayesian dictionary learning methods were shown to produce accurate estimates of the level of additive white Gaussian noise in images with minimal assumptions. We show that a similar model is capable of accu- rately modeling certain kinds of non-stationary noise processes, allowing for space-varying noise in images to be estimated, detected, and removed. We apply this modeling concept to several types of non-stationary noise and demonstrate the model’s effectiveness on real-world problems, including denoising and segmentation of images according to noise characteristics, which has applications in image forensics.

Paper Details

Date Published: 25 February 2014
PDF: 18 pages
Proc. SPIE 9019, Image Processing: Algorithms and Systems XII, 90190L (25 February 2014); doi: 10.1117/12.2039298
Show Author Affiliations
James M. Hughes, LGS Innovations (United States)
Dartmouth College (United States)
Daniel N. Rockmore, Dartmouth College (United States)
Yang Wang, Michigan State Univ. (United States)


Published in SPIE Proceedings Vol. 9019:
Image Processing: Algorithms and Systems XII
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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