Adaptive Iterative Order Statistics Filter

Preprocessing an image is an important element of image processing. When images are corrupted with fixed-value impulse noise or random-value impulse noise, it is essential to restore the quality of the degraded image using linear or non-linear filters, in order to make them suitable for subsequent processing. It has been proved that non-linear filters are effective in suppressing or eliminating fixed-value impulse noise [1]. Moreover, non-linear filters preserve the details and edges of an image during the process of denoising [2]. In this paper we propose, two non-linear filters Adaptive Iterative Median Filter with threshold (AIMF) and Adaptive Iterative Rank-ordered Filter with threshold (AIRF) to restore the images corrupted with fixedvalue impulse noise. These filters are based on the principle of order statistics. Simulated results show that the proposed filters perform much better than many other existing median-based filters and are found to give comparable results as that of JM filter based on Jarque-Bera test [20]. Moreover, the proposed filters are computationally simpler.