Enhancement of Over-Exposed and Under-Exposed Images Using Hybrid Gamma Error Correction Sigmoid Function
The demands to improve the visibility quality of the captured images in extremes lighting conditions have emerged increasingly important in digital image processing. The extremes conditions are when there is lack of reasonable lightnings termed as underexposed and too much of light termed as over...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
2007
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| Online Access: | http://psasir.upm.edu.my/id/eprint/6166/1/FK_2007_12%281-24%29.pdf http://psasir.upm.edu.my/id/eprint/6166/ |
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| Summary: | The demands to improve the visibility quality of the captured images in extremes
lighting conditions have emerged increasingly important in digital image processing.
The extremes conditions are when there is lack of reasonable lightnings termed as
underexposed and too much of light termed as overexposed. The popular
enhancement technique currently used is the contrast enhancement through contrast
stretching, histogram equalization, homomorphic filtering and contrast adjustment.
The adjustments are to transform the less useful images to more meaningful images
when the post image processing operations are carried out. This thesis is motivated to
deal with the problems concerning image capturing in these two extremes conditions.
The sigmoid function is used to adjust the contrast with two controlling parameters.
The parameters adjust the contrast locally and globally. The gamma function is
commonly used to correct the non-linear error in the images due to the camera lenses. This thesis combines the functions' properties and developed a hybrid
algorithm to improve the quality of the poorly captured images by adjusting the
contrast and compensating the gamma error. The sigmoid and gamma function are
coded in MATLAB 6.0 in which testes are made over the selected images. The
sample images are taken using different type of cameras transformed to grayscaled
input images. The luminosities of the surroundings are also measured using a light
meter.
The derivations of the parameters' ranges are done by calculating the root mean
square error or the standard deviation. The suggested ranges are used in the hybrid
system which has two variants, Variant I and Variant 11. The first variant, combines
the sigmoid function inside the gamma compensation function while the second
variant combines the gamma compensation function inside the sigmoid function.
Based on the test results, the proposed algorithm significantly improves the contrast
of the images. For the underexposed image samples, the percentages of the intensity
lesser than 0.1 decreases as more of the intensities reside at higher values. For the
overexposed image samples, the percentages of intensity greater than 0.9 decreases
as more of the intensities reside at lower values. With the suggested range deduced,
the images are contrast enhanced with the reduction of percentage of pixels residing
he intensity less than 0.1 and greater than 0.9.
The comparative analyses are made by comparing the suggested hybrid system with
the existing adaptive homomorphic filtering, adaptive histogram equalization and
adaptive contrast enhancement. |
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