Improved probabilistic decision-based trimmed median filter for detection and removal of high-density impulsive noise

Abstract

This study focuses on the detection and expulsion of noisy pixels from an image contaminated by impulsive noise. A noise detection approach is developed to avoid the misinterpretation of noise-free pixel as noisy. In order to design the noise removal algorithm, a probabilistic decision-based improved trimmed median filter (PDITMF) algorithm is proposed which is intended to work out the conflict related to the even number of noise-free pixels in the trimmed median filter. It deploys two new estimation techniques for de-noising, namely, improved trimmed median filter (ITMF) and patch else ITMF (PEITMF) as per noise density. At last, the noise detection approach is applied in the proposed PDITMF to build up a new technique called a probabilistic decision-based adaptive improved trimmed median filter (PDAITMF) algorithm. The proposed algorithms, PDITMF and PDAITMF experiment with many standard sample images. Simulation results show the proposed algorithms are capable of detecting and de-noising the contaminated image very efficiently and have a better visual representation. Under the authors' knowledge, the PDAITMF outperforms recently reported algorithms in context to peak signal-to-noise ratio as well as an image enhancement factor with the lower execution time at all noise densities.