Adaptive Marginal Median Filter for Colour Images

Samuel Morillas,Almanzor Sapena,Valentín Gregori

doi:10.3390/s110303205

Samuel Morillas, Almanzor Sapena + Show 1 more

Open Access

https://doi.org/10.3390/s110303205

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Abstract

This paper describes a new filter for impulse noise reduction in colour images which is aimed at improving the noise reduction capability of the classical vector median filter. The filter is inspired by the application of a vector marginal median filtering process over a selected group of pixels in each filtering window. This selection, which is based on the vector median, along with the application of the marginal median operation constitutes an adaptive process that leads to a more robust filter design. Also, the proposed method is able to process colour images without introducing colour artifacts. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter.

Highlights

Noise is often introduced into digital images during the acquisition and transmission processes because of different reasons such as CCD sensor malfunction, transmission errors, storage faults, and difficult acquisition conditions
In this paper we have presented an adaptive method for impulse noise reduction in colour images whose objective is to improve the noise reduction capability of the classical vector median filter
The filter is based on the selection of a few vectors in a population using the vector median and the application of a vector marginal median filtering over the selected vectors

Summary

Introduction

Noise is often introduced into digital images during the acquisition and transmission processes because of different reasons such as CCD sensor malfunction, transmission errors, storage faults, and difficult acquisition conditions. The presence of noise hampers the automatic processing of digital images and affects their visualization quality. This implies that the noise reduction task, known as image filtering, is a fundamental step in any computer vision system. In this context, several types of noise have been studied. The earliest filters were developed to process gray-scale images and were based on linear approaches We focus on the impulse noise case, which affects a portion of the image pixels, replacing their original values with other very different ones.

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