Improving image luminosity and contrast variation using hybrid statistical strategy

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...

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Format:	Thesis
Language:	English
Subjects:	Image analysis Performance technology Lighting Hybrid Statistical Enhancement (HSE) Luminosity
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78026/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78026/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78026/3/Wan%20Azani.pdf
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spelling	my-unimap-780262023-03-07T01:08:59Z Improving image luminosity and contrast variation using hybrid statistical strategy Haniza, Yazid, Dr. 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). Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78026 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78026/4/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78026/1/Page%201-24.pdf 58e0e57684eab018eff8185910792cad http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78026/2/Full%20text.pdf e8a79fc148ef877f0e55c623c7df94c1 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78026/3/Wan%20Azani.pdf 71da1b193c6e91a270703bc57c72e1f4 Universiti Malaysia Perlis (UniMAP) Image analysis Performance technology Lighting Hybrid Statistical Enhancement (HSE) Luminosity School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Haniza, Yazid, Dr.
topic	Image analysis Performance technology Lighting Hybrid Statistical Enhancement (HSE) Luminosity
spellingShingle	Image analysis Performance technology Lighting Hybrid Statistical Enhancement (HSE) Luminosity Improving image luminosity and contrast variation using hybrid statistical strategy
description	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).
format	Thesis
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
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78026/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78026/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78026/3/Wan%20Azani.pdf
_version_	1776104280841781248

Improving image luminosity and contrast variation using hybrid statistical strategy

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