Image Improvement Technique Using Feed Forward Neural Network

Abu Saeeda, Omar Abdulmola (2004) Image Improvement Technique Using Feed Forward Neural Network. Masters thesis, Universiti Putra Malaysia.

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Abstract

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.

Item Type:Thesis (Masters)
Subject:Image processing
Chairman Supervisor:Associate Professor Abdul Rahman Bin Ramli, PhD
Call Number:FK 2004 58
Faculty or Institute:Faculty of Engineering
ID Code:179
Deposited By:INVALID USER
Deposited On:23 May 2008 19:52
Last Modified:27 May 2013 06:46

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