Image Improvement Technique Using Feed Forward Neural Network

This research is aimed to develop an efficient image enhancement technique using multi layer Feedforward neural network. A nonlinear digital filter has been introduced as a promising solution for improving the image quality.The filter, which is named unsharp mask filter based neural network,signifi...

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Main Author:	Abu Saeeda, Omar Abdulmola
Format:	Thesis
Language:	English English
Published:	2004
Subjects:	Image processing
Online Access:	http://psasir.upm.edu.my/id/eprint/179/1/549507_t_fk_2004_58.pdf
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id	my-upm-ir.179
record_format	uketd_dc
spelling	my-upm-ir.1792013-05-27T06:46:17Z Image Improvement Technique Using Feed Forward Neural Network 2004-09 Abu Saeeda, Omar Abdulmola This research is aimed to develop an efficient image enhancement technique using multi layer Feedforward neural network. A nonlinear digital filter has been introduced as a promising solution for improving the image quality.The filter, which is named unsharp mask filter based neural network,significantly enhances the sharpness of image while highlights its details(edges and lines).In this thesis sharpening of image details has been obtained.Multi-layer Feed forward neural network with back propagation algorithm known as Multilayer Perceptron (MLP) is used to control the level of contrast enhancement.Grayscale blurred images were also used in this study.The results have been evaluated using mean square error as well as grayscale histogram distribution for sharpening of image details.Comparison among 3x3, 5x5 and 7x7 mask sizes has shown that least mean square error has been achieved by using the 3x3 mask size. However, the grayscale histogram distribution has shown that the proposed method has given more image details sharpening (11.333% in average)compared to the original free noise image.Regarding the size of filter mask, three filter masks which are, 3 x 3, 5 x 5 and 7 x 7 have been used in this study.Results have shown that the mean square error is proportionate with the increasing of mask size. The program has been implemented using MATLAB version 6.5 as programming language.Finally, unsharp mask filter based neural network with different mask sizes has been investigated. Results have shown that better performance has been obtained using the proposed method, i.e., 10% for 3x3, 11% for 5x5 and 13% for 7x7 mask size. Image processing 2004-09 Thesis http://psasir.upm.edu.my/id/eprint/179/ http://psasir.upm.edu.my/id/eprint/179/1/549507_t_fk_2004_58.pdf application/pdf en public masters Universiti Putra Malaysia Image processing Faculty of Engineering English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
topic	Image processing
spellingShingle	Image processing Abu Saeeda, Omar Abdulmola Image Improvement Technique Using Feed Forward Neural Network
description	This research is aimed to develop an efficient image enhancement technique using multi layer Feedforward neural network. A nonlinear digital filter has been introduced as a promising solution for improving the image quality.The filter, which is named unsharp mask filter based neural network,significantly enhances the sharpness of image while highlights its details(edges and lines).In this thesis sharpening of image details has been obtained.Multi-layer Feed forward neural network with back propagation algorithm known as Multilayer Perceptron (MLP) is used to control the level of contrast enhancement.Grayscale blurred images were also used in this study.The results have been evaluated using mean square error as well as grayscale histogram distribution for sharpening of image details.Comparison among 3x3, 5x5 and 7x7 mask sizes has shown that least mean square error has been achieved by using the 3x3 mask size. However, the grayscale histogram distribution has shown that the proposed method has given more image details sharpening (11.333% in average)compared to the original free noise image.Regarding the size of filter mask, three filter masks which are, 3 x 3, 5 x 5 and 7 x 7 have been used in this study.Results have shown that the mean square error is proportionate with the increasing of mask size. The program has been implemented using MATLAB version 6.5 as programming language.Finally, unsharp mask filter based neural network with different mask sizes has been investigated. Results have shown that better performance has been obtained using the proposed method, i.e., 10% for 3x3, 11% for 5x5 and 13% for 7x7 mask size.
format	Thesis
qualification_level	Master's degree
author	Abu Saeeda, Omar Abdulmola
author_facet	Abu Saeeda, Omar Abdulmola
author_sort	Abu Saeeda, Omar Abdulmola
title	Image Improvement Technique Using Feed Forward Neural Network
title_short	Image Improvement Technique Using Feed Forward Neural Network
title_full	Image Improvement Technique Using Feed Forward Neural Network
title_fullStr	Image Improvement Technique Using Feed Forward Neural Network
title_full_unstemmed	Image Improvement Technique Using Feed Forward Neural Network
title_sort	image improvement technique using feed forward neural network
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Engineering
publishDate	2004
url	http://psasir.upm.edu.my/id/eprint/179/1/549507_t_fk_2004_58.pdf
_version_	1747810167584456704

Image Improvement Technique Using Feed Forward Neural Network

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