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

• Published in: IET image processing Vol. 14; no. 17; pp. 4486 - 4498
• Main Authors: Sen, Amit Prakash, Rout, Nirmal Kumar
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 24.12.2020
• Subjects: contaminated image; detecting noising; de‐noising; expulsion; high‐density impulsive noise; image denoising; image enhancement; improved probabilistic decision‐based trimmed median filter; impulse noise; median filters; noise density; noise detection approach; noise removal algorithm; noise‐free pixel; noisy pixels; PDITMF; pepper noise; Research Article; signal‐to‐noise ratio; trimmed median filter algorithm; median filters; contaminated image; pepper noise; noise-free pixel; detecting noising; trimmed median filter algorithm; image denoising; noisy pixels; noise detection approach; de-noising; noise density; noise removal algorithm; image enhancement; high-density impulsive noise; impulse noise; signal-to-noise ratio; PDITMF; expulsion; improved probabilistic decision-based trimmed median filter
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/iet-ipr.2019.1240

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.