Efficient Algorithm for Enhancement of Images Corrupted by Salt & Pepper Noise

In this paper an efficient algorithm is proposed for removal of salt & pepper noise from digital images. The proposed algorithm consists of two stages: in first stage the noisy image is processed by adaptive median filter and in the second stage the output of first stage is further processed by modified mean filter. The first stage classifies noisy pixels by comparing each pixel in the image to its surrounding neighbourhood pixels. If the pixel is different from a majority of its neighbours, then it is considered as one of the noisy pixels. Noisy pixels are replaced by the median value of the neighbourhood pixels. Second stage works in two phases: in the first phase the noisy pixels are detected and in second phase each noisy pixel are replaced by the mean value of noise free pixel of 2×2 window. Simulation and experimental results show that the proposed algorithm consistently works well in suppressing the salt and pepper noise density (up to 95%). The proposed Adaptive Median based modified Mean filter (AMMF) outperforms a number of other existing filters such as standard median filter (SMF), centre weighted median filter (CWMF), progressive switching median filter (PSMF), open-close sequence filter (OCSF), decision based algorithm (DBA), modified decision based unsymmetric trimmed median filter (MDBUTMF) for noise removal in terms of high PSNR, low MSE and reduced streaking effect. The proposed algorithm is suitable for low level noise density as well as high level noise density. The proposed algorithm demonstrates better performance as compared to other existing techniques on different gray