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Improving image luminosity and contrast variation using hybrid statistical strategy

Doctor of Philosophy in Mechatronic Engineering

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Bibliographic Details
Main Author: Wan Azani, Wan Mustafa
Other Authors: Haniza, Yazid, Dr.
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2017
Subjects:
Image analysis
Performance technology
Lighting
Hybrid Statistical Enhancement (HSE)
Luminosity
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78026
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spelling my.unimap-780262023-03-07T01:08:59Z Improving image luminosity and contrast variation using hybrid statistical strategy Wan Azani, Wan Mustafa Haniza, Yazid, Dr. Image analysis Performance technology Lighting Hybrid Statistical Enhancement (HSE) Luminosity Doctor of Philosophy in Mechatronic Engineering Luminosity and contrast variation problems are among the most challenging tasks in the image processing field especially to improve the image quality. Enhancement is implemented by performing an adjustment of the dark or bright intensity in order to improve the quality of the images and to increase the segmentation performance. Recently, numerous methods had been proposed to normalize the luminosity and contrast variation. In this study, a new method based on a direct technique using a statistical data that is known as Hybrid Statistical Enhancement (HSE) is proposed. The HSE method used the mean and standard deviation of a local and global neighbourhood and classified the pixel into three groups; the foreground, border, and problematic region (contrast & luminosity). Two datasets namely document image and weld defect image were utilized to demonstrate the effectiveness of the HSE method. The results from the visual and objective aspects showed that the HSE method can normalize the luminosity and enhance the contrast variation problem effectively, compared to the other enhancement methods such as Homomorphic Filter and Discrete Cosine Transforms (DCT). Then, the segmentation process was done using the resulting image from the HSE method. In order to prove the HSE effectiveness, a few image quality assessments were presented and the results were discussed. The HSE method achieved the highest result compared to the other methods which are (Signal Noise Ratio = 9.32) for document dataset and (Signal Noise Ratio = 8.92) for weld defect dataset. In segmentation stage, the Otsu method obtained the highest average increment, which is 41% for document dataset and 82% for weld defect dataset. In conclusion, the implementation of the HSE method has produced an effective and efficient result for background correction, quality images improvement and increase the quality of segmentation result in term of Accuracy and Peak Signal Noise Ratio (PSNR). 2017 2023-03-07T01:08:59Z 2023-03-07T01:08:59Z Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78026 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Mechatronic Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Image analysis
Performance technology
Lighting
Hybrid Statistical Enhancement (HSE)
Luminosity
spellingShingle Image analysis
Performance technology
Lighting
Hybrid Statistical Enhancement (HSE)
Luminosity
Wan Azani, Wan Mustafa
Improving image luminosity and contrast variation using hybrid statistical strategy
description Doctor of Philosophy in Mechatronic Engineering
author2 Haniza, Yazid, Dr.
author_facet Haniza, Yazid, Dr.
Wan Azani, Wan Mustafa
format Thesis
author Wan Azani, Wan Mustafa
author_sort Wan Azani, Wan Mustafa
title Improving image luminosity and contrast variation using hybrid statistical strategy
title_short Improving image luminosity and contrast variation using hybrid statistical strategy
title_full Improving image luminosity and contrast variation using hybrid statistical strategy
title_fullStr Improving image luminosity and contrast variation using hybrid statistical strategy
title_full_unstemmed Improving image luminosity and contrast variation using hybrid statistical strategy
title_sort improving image luminosity and contrast variation using hybrid statistical strategy
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2017
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78026
_version_ 1772813090932391936
score 13.214268

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