Empirical Exploration Achievement of Noise Removal Algorithm Based on Trilateral Filter for Both Gaussian and Impulsive Noise Ambiance

Abstract

Although the Bilateral filter is one of the most realistic and virtuoso noise removal algorithms, which is often proposed for Gaussian noise in 1998, the Bilateral filter (BF) ineffectively works under the impulsive noise. Consequently, Trilateral filter (which is a modification Bilateral filter) was first proposed by Roman Garnett et al. in 2005 and this filter is based on the hybrid consisting of Bilateral filter and Rank-Ordered Absolute Differences (ROAD) statistic for automatically attenuating or excluding of Gaussian and impulsive noise. Thereby, this research paper empirically explores the efficient influence impact of these four parameters (spatial, radiometric, ROAD and joint impulsivity variance) of the Trilateral filter (TF) when this Trilateral filter (TF) is used for noise removal prospective attitude. In the noise removal exploration, Trilateral filter (TF) is used for five noisy standard images (Girl-Tiffany, Pepper, Baboon, House and Resolution) under five Gaussian noises and five Impulse noise, compared with state-of-the-art algorithms such as Bilateral filter (BF) and median filter. Subsequently, the highest result in the PSNR prospective attitude is nominated. Supplementary, an empirically exploration optimal value of ROAD variance and joint impulsivity variance that yield the highest PSNR is empirically explored for each images and each noise cases.