Statistical Analysis of Impulse Noise Model for Color Image Restoration
Mieng Quoc Phu, Peter Eric Tischer, Hong Ren Wu · 2007
In the area of color image restoration, many state-of-the-art filters consist of two main processes, classification and reconstruction. Classification is used to separate clean pixels from corrupted pixels. Reconstruction involves using values from corrupted pixels to interpolate values for pixels believed to have been corrupted. In this paper, two statistical analyses are carried out to determine how salt and pepper and random impulse noise behave in images. By computing the cluster count and probability of occurrence in a database of 1000 single color noisy images for each noise model, we found that the results will benefit the classification and reconstruction process in color image filters.