A novel learning-based switching median filter for suppression of impulse noise in highly corrupted colour images

Abstract

A novel switching median filter integrated with a learning-based noise detection method is proposed for suppression of impulse noise in highly corrupted colour images. Noise detection employs a new machine learning algorithm, called margin setting (MS), to detect noise pixels. MS detection is achieved by classifying noise and clean pixels with a decision surface. MS detection yields very high detection accuracy, i.e. a zero miss detection rate and a fairly low over detection rate for a wide range of noise levels. After noise detection, a new filter scheme called the noise-free two-stage (NFTS) filter is triggered. NFTS corrects the noise pixels using the median of the noise-free pixels in two stages. The results of experiments have demonstrated that the MS based NFTS (MSN) filter is superior to the support vector machine and neural network for denoising highly corrupted images, in terms of noise suppression and detail preservation.