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 |
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| Language: | English |
| Subjects: | |
| 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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| Summary: | 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). |
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