Empirical Exploration Achievement of Noise Removal Algorithm Based on Trilateral Filter for Both Gaussian and Impulsive Noise Ambiance
Empirical Exploration Achievement of Noise Removal Algorithm Based on Trilateral Filter for Both Gaussian and Impulsive Noise Ambiance
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2016-06
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eng
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application/pdf
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6 pages
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Proceeding of The 13th Annual International Conference of Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2016), ECTI Association Thailand, Chiang Mai, Thailand, June 2016
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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.