Fusion of clahe-based image enhancement with fuzzy set theory on field image
Field image can be defined as image captured from handphone or any mobile device in open or outdoor environment. Field image is also known as low quality, low resolution, noise and affected background. In contrast, image captured in lab or studio is a high-quality image taken in a proper setup us...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/104003/1/ELMALIANA%20BINTI%20ALBAHARI%20-%20IR.pdf |
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| Summary: | Field image can be defined as image captured from handphone or any mobile
device in open or outdoor environment. Field image is also known as low quality,
low resolution, noise and affected background. In contrast, image captured in
lab or studio is a high-quality image taken in a proper setup using high
specification device. In agriculture, field leaf image is commonly used to identify
plant disease. Accurate detection of plant disease is needed to strengthen the
field of agriculture and economy of the country. The disadvantages of field leaf
image, are low resolution, low contrast, blur and unsharp due to inconsistent
setting or environment exposures. Image enhancement method helps to
improve image quality, reduce impulsive noise, and sharpen the edges of field
leaf image. In this study, measurement of contrast level is used to compare the
quality between field leaf image and image taken in the studio (lab image). High
quality image has high contrast value and it shows that lab image has high
contrast value. Therefore, this research is focus on field leaf image
enhancement to improve the quality of the image and make it as same quality
as lab image. This research presents a framework of fusion techniques namely,
Contrast-Limited Adaptive Histogram Equalization (CLAHE), Unsharp Masking
(USM) and Fuzzy theory. CLAHE-based and USM image enhancement
techniques are widely used to enhance and sharpen the edge of field leaf image.
However, the drawback of these techniques is the field leaf image is still in low
contrast and not as same quality as the lab image. To further improve the quality
of field leaf image, combine the existing framework with Fuzzy Set Theory.
Furthermore, there are significant difference when applying the framework in
global and local images. Therefore, comparison the performance of the
framework is done between global and local images. The result of the proposed
image enhancement framework is compared with the lab image as a benchmark.
From the results shows that the proposed image enhancement framework
produces better quality of field leaf image and required minimum processing
time. The evaluation measurement methods used in this research are Contrast
Value, Contrast Difference (DC), Contrast Improvement Index (CII) and Peak-
Signal-Noise-Ratio (PSNR). The proposed fusion framework proved that field
leaf image produces better quality image where the CII value increased from
86% to 94%. It also shows that local-based image enhancement with 4x4
patches produce better quality from global-based image enhancement. |
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