Contrast modification for pre-enhancement process in multicontrast rubeosis iridis images
Existing researchers for rubeosis iridis disease focused on image enhancement as a collective group without considering the multi-contrast of the images. In this paper, the pre-enhancement process was proposed to improve the quality of iris images for rubeosis iridis disease by separating the image...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Ahmad Dahlan
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38388/1/Contrast%20modification%20for%20pre-enhancement%20process%20in%20multi-contrast.pdf http://umpir.ump.edu.my/id/eprint/38388/ https://doi.org/10.12928/TELKOMNIKA.v21i4.22251 https://doi.org/10.12928/TELKOMNIKA.v21i4.22251 |
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| Summary: | Existing researchers for rubeosis iridis disease focused on image enhancement as a collective group without considering the multi-contrast of the images. In this paper, the pre-enhancement process was proposed to improve the quality of iris images for rubeosis iridis disease by separating the image into three groups; low, medium and high contrast. Increment, decrement and maintenance of the images’ original contrast were further operated by noise reduction and multi-contrast manipulation to attain the best contrast value in each category for increased compatibility prior subsequent enhancement. As a result, this study proved that there have three rules for the contrast modification method. Firstly, the histogram equalization (HE) filter and increasing the image contrast by 50% will achieve the optimum value for the low contrast category. Experimental revealed that HE filters successfully increase the luminance value before undergoing the contrast modification method. Secondly, reducing the 50% of the image contrast to achieve the optimum value for the high contrast category. Finally, the image contrast was maintained for the middle contrast category to optimise contrast. The mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the outputs were then calculated, yielding an average of 18.25 and 28.87, respectively. |
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