Image noise reduction by iteratively truncated mean filter

It is well known that noise filtering is very important in image and signal processing. Common noise filters are mean and median filters which they have respective pros and cons in noise attenuation and image structure preservation. Therefore there is a necessity and tendency to develop another t...

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Main Author: Shen, Meng
Other Authors: Jiang Xudong
Format: Final Year Project
Language:English
Published: 2015
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Online Access:http://hdl.handle.net/10356/62105
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-621052023-07-07T16:56:28Z Image noise reduction by iteratively truncated mean filter Shen, Meng Jiang Xudong School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems It is well known that noise filtering is very important in image and signal processing. Common noise filters are mean and median filters which they have respective pros and cons in noise attenuation and image structure preservation. Therefore there is a necessity and tendency to develop another type of noise filter to combine the merits of both mean and median filters. Iterative Truncated Arithmetic Mean (ITM) filter is introduced and developed to have the merits of both mean and median filters, which starts from the mean to approach the median. Another type of filter Fast Iterative Truncated Arithmetic Mean (FITM) filter based on ITM filter is also proposed to be expected to have faster running speed compared to ITM filter. Both ITM and FITM filter will be implemented by MATLAB and C code respectively. A lot of experiment will be conducted as well to compare the results of two different implementation methods, the performance compared to median filter as well as the running speed. Bachelor of Engineering 2015-01-21T03:00:35Z 2015-01-21T03:00:35Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/62105 en Nanyang Technological University 57 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Shen, Meng
Image noise reduction by iteratively truncated mean filter
description It is well known that noise filtering is very important in image and signal processing. Common noise filters are mean and median filters which they have respective pros and cons in noise attenuation and image structure preservation. Therefore there is a necessity and tendency to develop another type of noise filter to combine the merits of both mean and median filters. Iterative Truncated Arithmetic Mean (ITM) filter is introduced and developed to have the merits of both mean and median filters, which starts from the mean to approach the median. Another type of filter Fast Iterative Truncated Arithmetic Mean (FITM) filter based on ITM filter is also proposed to be expected to have faster running speed compared to ITM filter. Both ITM and FITM filter will be implemented by MATLAB and C code respectively. A lot of experiment will be conducted as well to compare the results of two different implementation methods, the performance compared to median filter as well as the running speed.
author2 Jiang Xudong
author_facet Jiang Xudong
Shen, Meng
format Final Year Project
author Shen, Meng
author_sort Shen, Meng
title Image noise reduction by iteratively truncated mean filter
title_short Image noise reduction by iteratively truncated mean filter
title_full Image noise reduction by iteratively truncated mean filter
title_fullStr Image noise reduction by iteratively truncated mean filter
title_full_unstemmed Image noise reduction by iteratively truncated mean filter
title_sort image noise reduction by iteratively truncated mean filter
publishDate 2015
url http://hdl.handle.net/10356/62105
_version_ 1772826009830162432