Switching median filter: advanced boundary discriminative noise detection algorithm

Abstract

A novel, efficient and simple impulse noise detector for switching median filter is proposed in this study. The proposed method compares the difference value of the current pixel with the brightest and the darkest pixels in its working window and uses the difference value to determine whether the current pixel is corrupted by impulse noise. To avoid the false alarm generated in this first stage, the candidate pixels are passed through a second stage using local statistics. This new technique can remove the impulsive noise from corrupted images (grey scale and colour) efficiently and requires no previous training and noise statistics or strength for that purpose. For performance evaluation, four impulse noise models (Ng and Ma, 2006) are considered and noise models 3 and 4 are generalised to expand its application. Proposed algorithm is blind to the noise model and the amount of noise introduced. Quantitative and qualitative analysis performed on standard images, show that proposed method can detect impulse noise very efficiently under a wide range (up to 90%) of noise density. The proposed method performs well in terms of low miss and false detection and high peak-signal-to-noise ratio (PSNR) value. In comparison to all examined algorithms, the proposed technique performs favourably and outperforms the competitors for all the noise models. The gain is most prominent in case of impulse noise having spread as in case of noise models 3 and 4 (Ng and Ma, 2006).