Adaptive rank weighted switching filter for impulsive noise removal in color images

Bogdan Smolka,Dariusz Malik,Krystyna Malik

doi:10.1007/s11554-012-0307-0

Bogdan Smolka, Dariusz Malik + Show 1 more

Open Access

https://doi.org/10.1007/s11554-012-0307-0

Copy DOI

Abstract

In this paper, a novel approach to the problem of impulsive noise removal in color digital images is presented. The described switching filter is based on the rank weighted, cumulated pixel dissimilarity measures, which are used for the detection of image samples contaminated by impulsive noise process. The introduced adaptive design enables the filter to tune its parameters to the amount of impulsive noise corrupting the image. The comparison with existing denoising schemes shows that the new technique more efficiently removes the impulses introduced by the noise process, while better preserving image details. An important feature of the new filter is its low computational complexity, which allows for its application in real-time applications.

Highlights

The increase in use of color images in multimedia technologies and telecommunication has accelerated significantly in recent years
The described switching filter is based on the rank weighted, cumulated pixel dissimilarity measures, which are used for the detection of image samples contaminated by impulsive noise process
We propose an impulsive noise detection scheme which efficiently detects the corrupted pixels, while keeping the samples not affected by the noise process unchanged

Summary

Introduction

The increase in use of color images in multimedia technologies and telecommunication has accelerated significantly in recent years. Xj2W k1⁄41 where kÁk denotes the Euclidean norm and djk denotes the distance between xj and xk: If the image contamination intensity is high, the output of the filters which are based on the reduced ordering can be corrupted by noise, as the vector median belongs to the set of noisy pixels contained in the processing window. This effect can be alleviated applying the so-called marginal median filter (MMF), which outputs the pixel, whose components are the medians of the scalar values of the corresponding channels. Some final conclusions are drawn in the last Section of the paper

Impulsive noise models

Rank weighted vector median filter

Impulsive noise detection

Adaptive switching filter

Comparison with existing techniques

Conclusions