Adaptive iterated function systems filter for images highly corrupted with fixed – Value impulse noise

The Adaptive Iterated Functions Systems (AIFS) Filter presented in this paper has an outstanding potential to attenuate the fixed-value impulse noise in images. This filter has two distinct phases namely noise detection and noise correction which uses Measure of Statistics and Iterated Function Systems (IFS) respectively. The performance of AIFS filter is assessed by three metrics namely, Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index Matrix (MSSIM) and Human Visual Perception (HVP). The quantitative measures PSNR and MSSIM endorse the merit of this filter in terms of degree of noise suppression and details/edge preservation respectively, in comparison with the high performing filters reported in the recent literature. The qualitative measure HVP confirms the noise suppression ability of the devised filter. This computationally simple noise filter broadly finds application wherein the images are highly degraded by fixed-value impulse noise