The Comparison of Iris Detection Using Histogram Equalization and Adaptive Histogram Equalization Methods

Mustaghfirin, Fathan; Erwin, Erwin; Putra, Hadrians Kesuma; Yanti, Umi; Ricadonna, Rahma

doi:10.1088/1742-6596/1196/1/012016

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Using PSNR as a measure evaluates the accuracy of the image enhancement in terms of pixellevel fidelity. The proposed approaches achieved higher PSNR values than existing state-of-the-art methods [1], [18], [23], as shown in Table II, indicating that AFHE, CLAHE, and FCE preserve image quality and reduce distortion throughout the enhancement process. PSNR is commonly employed to evaluate methods such as HE, CLAHE, and FCE that modify image brightness; however, it may not adequately measure perceptual quality or task performance.…”

Section: Resultsmentioning

confidence: 83%

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Future Iris Imaging with Advanced Fuzzified Histogram Equalization

Mashudi,

Ahmad,

Dziyauddin

et al. 2024

IJACSA

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Images captured under low lighting frequently exhibit low brightness, low contrast, and a small grayscale. These features can affect the individual's view and severely limit the performance of machine vision systems, particularly when data annotation is involved. Hence, the issues motivate this study to examine the effectiveness of advanced fuzzified histogram equalization for image enhancement. A comparative study was conducted based on the low lighting condition of iris images to evaluate three image enhancement methods: Advanced Fuzzified Histogram Equalization (AFHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Fuzzy Contrast Enhancement (FCE) using the MIREIS dataset. The Gaussian membership functions (GMF) were modified accordingly to satisfy the suitable pixel intensity of the input iris images. The results were compared using the peak signal-to-noise ratio (PSNR) value, including the central processing unit (CPU) times. As a result, the AFHE showed a better PSNR value at 76.02db with faster CPU times at 4.04s compared to CLAHE and FCE. Although the PSNR value of HE is slightly lower than CLAHE (0.3%) and FCE (0.7%), AFHE improved the image's quality and brightness, which can help other researchers with the data annotation process. The performance of the proposed methods was validated by comparing them with state-of-the-art methods. The results demonstrated that AFHE, CLAHE, and FCE exceeded other HE, AHE, CLAHE, and hybrid HE using fuzzy approaches that employed PSNR metrics.

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Section: Resultsmentioning

confidence: 83%

“…The CPSSA algorithm utilizes population-based iteration to search for specific clipping thresholds that meet the specified criteria, resulting in CLAHE generating a collection of iris images. A study in [23] applied HE and AHE to compare with Canny edge detection. The AHE aims to determine the iris patterns with high-contrast images.…”

Section: Related Workmentioning

confidence: 99%

Future Iris Imaging with Advanced Fuzzified Histogram Equalization

Mashudi,

Ahmad,

Dziyauddin

et al. 2024

IJACSA

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“…However, the major drawback of this method is losing the originality of image, loss of image information, over enhancement of brightness as well as the contrast and amplified the noise from the original image. Many attempts done by researchers to reduce the HE drawback by introducing several methods such as adaptive histogram equalization (AHE) [12], contrast limited adaptive histogram equalization (CLAHE) [13], brightness preserving bi-histogram equalization [14], sub-image histogram equalization method [15], recursive mean separate histogram equalization [16] and bi-histogram equalization [17].…”

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confidence: 99%

Contrast modification for pre-enhancement process in multi-contrast rubeosis iridis images

2023

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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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