Robust estimation approach for blind denoising

This work develops a new robust statistical framework for blind image denoising. Robust statistics addresses the problem of estimation when the idealized assumptions about a system are occasionally violated. The contaminating noise in an image is considered as a violation of the assumption of spatia...

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Bibliographic Details
Published in:IEEE transactions on image processing 2005-11, Vol.14 (11), p.1755-1765
Main Author: Rabie, T.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Blind denoising
Computer Simulation
Degradation
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Gaussian noise
Gaussian noise filtering
Image denoising
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image restoration
Information Storage and Retrieval - methods
Information, signal and communications theory
Models, Statistical
Noise reduction
Noise robustness
Optical noise
outliers
redescending estimators
robust denoising
robust statistics
Signal and communications theory
Signal processing
Signal, noise
Spatial coherence
Statistics
Stochastic Processes
Telecommunications and information theory
Wiener filter
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Summary:This work develops a new robust statistical framework for blind image denoising. Robust statistics addresses the problem of estimation when the idealized assumptions about a system are occasionally violated. The contaminating noise in an image is considered as a violation of the assumption of spatial coherence of the image intensities and is treated as an outlier random variable. A denoised image is estimated by fitting a spatially coherent stationary image model to the available noisy data using a robust estimator-based regression method within an optimal-size adaptive window. The robust formulation aims at eliminating the noise outliers while preserving the edge structures in the restored image. Several examples demonstrating the effectiveness of this robust denoising technique are reported and a comparison with other standard denoising filters is presented.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2005.857276