Development of Impulsive Noise Detection Schemes
for Selective Filtering in Images

Abstract

Image Noise Suppression is a highly demanded approach in digital imaging
systems design. Impulsive noise is one such noise, which is frequently encountered
problem in acquistion, transmission and processing of images. In the area of image
restoration, many state-of-the art filters consist of two main processes, classification
(detection) and reconstruction (filtering). Classification is used to separate
uncorrupted pixels from corrupted pixels. Reconstruction involves replacing the
corrupted pixels by certain approximation technique. In this thesis such schemes
of impulsive noise detection and filtering thereof are proposed.
Impulsive noise can be Salt & Pepper Noise (SPN) or Random Valued Impulsive
Noise (RVIN). Only RVIN model is considered in this thesis because of its realistic
presence. In the RVIN model a corrupted pixel can take any value in the valid
range.
Adaptive threshold selection is emphasized for all the four proposed noise detection
schemes. Incorporation of adaptive threshold into the noise detection
process led to more reliable and more efficient detection of noise. Based on the
noisy image characteristics and their statistics, threshold values are selected.
To validate the efficacy of proposed noise filtering schemes, an application to
image sharpening has been investigated under the noise conditions. It has been
observed, if the noisy image passes through the sharpening scheme, the noise
gets amplified and as a result the restored results are distorted. However, the
prefiltering operations using the proposed schemes enhances the result to a greater
extent.
Extensive simulations and comparisons are done with competent schemes. It is
observed, in general, that the proposed schemes are better in suppressing impulsive
noise at different noise ratios than their counterparts.