Adaptive varying-window-size image denoising using the ICI rule with median filters

Karen O. Egiazarian; Vladimir Katkovnik; Juan Luis Medina

doi:10.1117/12.467985

22 May 2002 Adaptive varying-window-size image denoising using the ICI rule with median filters

Karen O. Egiazarian, Vladimir Katkovnik, Juan Luis Medina

Proceedings Volume 4667, Image Processing: Algorithms and Systems; (2002) https://doi.org/10.1117/12.467985
Event: Electronic Imaging, 2002, San Jose, California, United States

Abstract

We describe a novel approach to solve a problem of window size selection for median based filtering of noisy images. The approach is based on the intersection of confidence intervals (ICI) rule and results in algorithms that are simple in implementation. The ICI rule gives the adaptive varying bandwidths and enables the algorithm to be spatially adaptive in the sense that its quality is close to that which one could achieve if the smoothness of the estimated signal were known in advance. We propose and analyze a two-stage structure for the median based adaptive filter with different use of the ICI rule. At the first stage (segmentation), the ICI rule with the median filter is applied in order to find the adaptive window size for every pixel of the image. At the second stage (filtering), the image denoising is produced by a weighted median filter with varying window sizes obtained at the first stage. Two different approaches (with a single centered window and with combined four-quadrant windows), affecting the structure of the filters, have been considered in order to form the local neighborhood of the targeted pixel. Comparison of the developed algorithm with known techniques for noise removal shows the advantage of the new adaptive window size approach, both quantitatively and visually.

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Karen O. Egiazarian, Vladimir Katkovnik, and Juan Luis Medina "Adaptive varying-window-size image denoising using the ICI rule with median filters", Proc. SPIE 4667, Image Processing: Algorithms and Systems, (22 May 2002); https://doi.org/10.1117/12.467985

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KEYWORDS

Digital filtering

Image filtering

Gaussian filters

Filtering (signal processing)

Image denoising

Image segmentation

Algorithm development

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