Improving image luminosity and contrast variation using hybrid statistical strategy

Doctor of Philosophy in Mechatronic Engineering

Saved in:

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
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Universiti Malaysia Perlis
Language:	English

id	my.unimap-78026
record_format	dspace
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

Improving image luminosity and contrast variation using hybrid statistical strategy

Similar Items