Triangular Fuzzy Membership-Contrast Limited Adaptive Histogram Equalization (TFM-CLAHE) for Enhancement of Multimodal Biometric Images

Vidya, B. Sree; Chandra, E.

doi:10.1007/s11277-019-06184-6

Cited by 16 publications

(6 citation statements)

References 27 publications

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“…Contrast limited adaptive histogram equalization (CLAHE) algorithm was used as the first technique for enhancing the contrast between veins and the background. The detailed step of the CLAHE algorithms are explained as follows [32]:…”

Section: Contrast Limited Adaptive Histogram Equalization (Clahe)mentioning

confidence: 99%

Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

Yakno

Mohamad–Saleh

Ibrahim

2021

Sensors

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Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.

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Section: Contrast Limited Adaptive Histogram Equalization (Clahe)mentioning

confidence: 99%

Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

Yakno

Mohamad–Saleh

Ibrahim

2021

Sensors

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“…However, the clipping value in the CLAHE cannot be selected adaptively. In [13], Vidya et al proposed a novel triangular fuzzy membership (TFM) function-based CLAHE (TFM-CLAHE) to solve this problem. The algorithm has good effect on the iris, face, fingerprint, and other biological images.…”

Section: Introduction 1research Backgroundmentioning

confidence: 99%

“…The algorithm has good effect on the iris, face, fingerprint, and other biological images. However, the work in [13] only uses some objective indicators such as Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), and Average Information Content (AIC) to evaluate the effect of TFM-CLAHE.…”

Section: Introduction 1research Backgroundmentioning

confidence: 99%

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Xiong

Zhang

et al. 2021

Mathematics

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At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

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“…In a similar way, The Triangular Fuzzy Membership CLAHE (TFM-CLAHE) algorithm employs the triangular fuzzy membership function to determine the clip limit. 18 Because the parameters of the membership function in the FIS is fixed, the clip limit value is not reasonable in dealing with some images with a different dynamic range. Chaira 19 presented a contrast enhancement algorithm based on the Type II fuzzy set.…”

Section: Introductionmentioning

confidence: 99%

“…Based on CLAHE, Jenifer et al 17 presented a new algorithm, named Fuzzy Clipped CLAHE, which uses the fuzzy inference system (FIS) to determine the clip limit automatically. In a similar way, The Triangular Fuzzy Membership CLAHE (TFM‐CLAHE) algorithm employs the triangular fuzzy membership function to determine the clip limit 18 . Because the parameters of the membership function in the FIS is fixed, the clip limit value is not reasonable in dealing with some images with a different dynamic range.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement

Cheng

et al. 2020

Int J Imaging Syst Tech

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In order to overcome the drawback of the existing image enhancement technologies and further consider the pixel intensity expression error caused by imaging, a novel fuzzy inference‐based contextual dissimilarity histogram equalization (FICDHE) algorithm is proposed. The proposed algorithm is composed of three modules. In the first module, according to the calculated probable intensity intervals, the membership functions of intensity are generated. In the second one, fuzzy inference systems are established and the contextual dissimilarity of each pixel is calculated. In the third module, the contextual dissimilarity histograms are clipped and equalized. The fuzzy system established in this paper not only fully considers the uncertainty source of pixel gray level expression, but also has adaptability. The parameter selection of fuzzy inference system membership function in this algorithm does not need human intervention, but is automatically obtained based on the statistical information of image pixel gray. Its adaptability makes the algorithm more widely used and convenient. Experiments are conducted using four typical medical images and 800 images from the KNIX dataset and BrainWeb dataset. The performance of the proposed method was compared to a series of enhancement algorithms based on both subjective judgment and image quality measurement indexes. Experimental results demonstrate that the proposed algorithm has a better contrast enhancement ability and yields better performance.

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Triangular Fuzzy Membership-Contrast Limited Adaptive Histogram Equalization (TFM-CLAHE) for Enhancement of Multimodal Biometric Images

Cited by 16 publications

References 27 publications

Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement

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