Robust estimation approach for blind denoising

• 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
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2005.857276

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.