A Multiscale Detection based Adaptive Median Filter for the Removal of Salt and Pepper Noise from Highly Corrupted Images

A new switching median filter is proposed for denoising of gray-scale images, extremely corrupted by salt-and-pepper noise. The proposed model for noise removal is a multiscale detection based adaptive median filter. This method consists of mainly two parts, namely, the thresholding based multiscale noise detection and the filtering. The detection of impulse noise is carried out in two stages. First, multiscale filtering of the corrupted image is carried out using Gaussian kernels at different scales and errors between the original and the filtered images at different scales are obtained. In the next stage, the errors at different scales are added and then thresholded to detect the impulse noise. The filtering of impulses, detected in the first stage of the proposed filter, is finally carried out using an adaptive median filter. Incorporation of a multiscale method into the noise detection stage followed by thresholding has led to more reliable and efficient impulse noise detection, especially, at high noise ratios. To validate the efficacy of proposed scheme, extensive simulations and comparisons are done with the competent schemes under a wide range (10% to 90%) of noise densities. The results show that the proposed scheme works much better in suppressing high level noise than other schemes, keeping the edges and fine details of the original image almost intact.