A Scientific Approach for Generation of Random Fields for Image Enhancement and Reconstruction

Noise Suppression from images is one of the most important concerns in digital image processing. Impulsive noise is one such noise, which may corrupt images during their acquisition or transmission or storage etc. A variety of techniques are reported to remove this type of noise. It is observed that techniques which follow the two stage process of detection of noise and filtering of noisy pixels achieve better performance than others. In this work such schemes of impulsive noise detection and filtering thereof are proposed. Two models of impulsive noise are considered in this work. The first one is Salt & Pepper Noise (SPN) model, where the noise value may be either the minimum or maximum of the dynamic gray scale range of the image. And, the second one is Random Valued Impulsive Noise (RVIN) model, where the noise pixel value is bounded by the range of the dynamic gray scale of the image. This work deal with SPN model and deal with RVIN model of noise. The first scheme is based on second order difference of pixels in order to identify noisy pixels. The second scheme for SPN model uses fuzzy technique to locate contaminated pixels. The contaminated pixels are then subject to median filtering. This detection–filtration is done recursively so that filtered pixels take part in the detection of noise in the next pixel. In the propose schemes for adaptive threshold selection is emphasizing. Incorporation of adaptive threshold into the noise detection process may be leads to more reliable and more efficient detection of noise. Based on the noisy image characteristics and their statistics, threshold values are selected. It may be observed, in general, that the proposing schemes are better in suppressing impulsive noise at different noise ratios than their counterparts.