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dc.contributor.authorVorapoj Patanavijit
dc.identifier.citationProceeding 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 2016en_US
dc.description.abstractAlthough 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.en_US
dc.format.extent6 pagesen_US
dc.subjectTrilateral filter (TF)en_US
dc.subjectBilateral filter (BF)en_US
dc.subjectRank-Ordered Absolute Differences (ROAD)en_US
dc.subjectDigital image processingen_US
dc.titleEmpirical Exploration Achievement of Noise Removal Algorithm Based on Trilateral Filter for Both Gaussian and Impulsive Noise Ambianceen_US
dc.rights.holderVorapoj Patanavijiten_US
mods.genreProceeding Paperen_US[Full Text] (

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