A switching median filter with boundary discriminative noise detection for extremely corrupted images

• Published in: IEEE transactions on image processing Vol. 15; no. 6; pp. 1506 - 1516
• Main Authors: NG, Pei-Eng, MA, Kai-Kuang
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Boundaries; Color; Colored noise; Corruption; Degradation; Detection, estimation, filtering, equalization, prediction; Discriminant Analysis; Exact sciences and technology; Filtration - methods; Image denoising; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image quality; Imaging, Three-Dimensional - methods; impulse noise detection; Impulses; Information filtering; Information filters; Information Storage and Retrieval - methods; Information, signal and communications theory; Miscellaneous; Noise; Noise reduction; Noise robustness; nonlinear filter; Nonlinear filters; Pattern recognition; Pixel; Pixels; Reproducibility of Results; Samarium; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal Processing, Computer-Assisted; Signal, noise; Studies; Switching; switching median filter; Telecommunications and information theory; Performance evaluation; Discriminant analysis; Image processing; nonlinear filter; switching median filter; Non linear filter; High precision; Algorithm; Shape detection; Color image; Implementation; False alarm rate; Pulse noise; Simulation; impulse noise detection; Signal processing; Feature extraction; Real time processing; Localization; Edge detection; Median filter; Black and white image; Image denoising
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2005.871129

A novel switching median filter incorporating with a powerful impulse noise detection method, called the boundary discriminative noise detection (BDND), is proposed in this paper for effectively denoising extremely corrupted images. To determine whether the current pixel is corrupted, the proposed BDND algorithm first classifies the pixels of a localized window, centering on the current pixel, into three groups-lower intensity impulse noise, uncorrupted pixels, and higher intensity impulse noise. The center pixel will then be considered as "uncorrupted," provided that it belongs to the "uncorrupted" pixel group, or "corrupted." For that, two boundaries that discriminate these three groups require to be accurately determined for yielding a very high noise detection accuracy-in our case, achieving zero miss-detection rate while maintaining a fairly low false-alarm rate, even up to 70% noise corruption. Four noise models are considered for performance evaluation. Extensive simulation results conducted on both monochrome and color images under a wide range (from 10% to 90%) of noise corruption clearly show that our proposed switching median filter substantially outperforms all existing median-based filters, in terms of suppressing impulse noise while preserving image details, and yet, the proposed BDND is algorithmically simple, suitable for real-time implementation and application.