Probabilistic Decision Based Improved Trimmed Median Filter to Remove High-Density Salt and Pepper Noise

Abstract

This paper focuses on the removal of salt and pepper noise from a contaminated image. A Probabilistic Decision Based Improved Trimmed Median Filter (PDITMF) is proposed here. The proposed PDITMF algorithm resolves the conflict regarding an even number of noise free pixel of Trimmed Median Filter. The proposed algorithm makes use of two estimation techniques for de-noising, namely, Improved Trimmed Median Filter (ITMF), and Patch Else Improved Trimmed Median Filter (PEITMF) depending upon noise density. The algorithm experiments with many standard sample images. Simulation results show the proposed algorithm is capable of de-noising the image very efficiently. The algorithm has a better visual representation and it outperforms the existing well-known algorithms in context to peak signal-to-noise ratio (PSNR) as well as image enhancement factor (IEF) with lower execution time (ET) at all noise densities.