Image Enhancement Technique at Different Distance for Iris Recognition
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Abstract:
Capturing eye images within visible wavelength illumination in non-cooperative environment lead to the low quality of eye images. Thus, this study is motivated to investigate the effectiveness of image enhancement technique that able to solve the abovementioned issue. A comparative study has been conducted in which three image enhancement techniques namely Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) were evaluated and analysed. UBIRIS.v2 eye image database was used as a dataset to evaluate those techniques. Moreover, each of enhancement techniques were tested against different distance of eye image captured. Results were compared in term of image interpretation by using Peak-Signal Noise Ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Mean Absolute Error (MAE). The effectiveness of the enhancement techniques on different distance of image captured was evaluated using the False Acceptance Rate (FAR) and False Rejection Rate (FRR). As a result, CLAHE has proven to be the most reliable technique in enhancing the eye image which improved the localization accuracy by 7%. In addition, the results showed that by implementing CLAHE technique at four meter distance was an ideal distance to capture eye images in non-cooperative environment where it provides high recognition accuracy, 74%.Keywords:
Adaptive histogram equalization
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Histogram equalization (HE), a simple contrast enhancement (CE) method, tends to show excessive enhancement and gives unnatural artifacts on images with high peaks in their histograms. Histogram-based CE methods have been proposed in order to overcome the drawback of HE, however, they do not always give good enhancement results. In this letter, a histogram-based locality-preserving CE method is proposed. The proposed method is formulated as an optimization problem to preserve localities of the histogram for performing image CE. The locality-preserving property makes the histogram shape of the enhanced image to be similar to that of the original image. Experimental results show that the proposed histogram-based method gives output images with graceful CE on which existing methods give unnatural results.
Adaptive histogram equalization
Balanced histogram thresholding
Image histogram
Color normalization
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Infrared (IR) images are basically low-contrast in nature; hence, it is essential to enhance the contrast of IR images to facilitate real-life applications. This work proposes a novel adaptive clip-limit-oriented bi-histogram equalization (bi-HE) method for enhancing IR images. HE methods are simple in implementation but often cause over-enhancement due to the presence of long spikes. To reduce long spikes, this work suggests to apply a log-power operation on the histogram, where the log operation reduces the long spikes, and power transformation regains the shape of the histogram. First, a histogram separation point is generated applying the mean of the multi-peaks of the input histogram. After that, an alteration in the input histogram is done using the log-power process. Subsequently, a clipping operation on the altered histogram followed by redistribution of the clipped portion is performed to restrict over-enhancement. Next, the modified histogram is sub-divided using the histogram separation point. Finally, the modified sub-histograms are equalized independently. Simulation results show that the suggested method effectively improves the contrast of IR images. Visual quality evaluations and quantitative assessment demonstrate that the suggested method outperforms the state-of-the-art algorithms.
Adaptive histogram equalization
Balanced histogram thresholding
Image histogram
Clipping (morphology)
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Adaptive histogram equalization
Balanced histogram thresholding
Image histogram
Color normalization
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This paper presents the adaptive histogram adjustment (AHA) to improve image contrast. The proposed method is achieved based on the concepts of weighted histogram separation and gray-level grouping. It not only improves the contrast of the local detail but also solves group density and blocky effect which occur at the under-quantization problem of weighted histogram separation. Moreover, the adaptive histogram adjustment can prevent the contrast over-enhancement of conventional adaptive-based approach. The experimental results show that AHA has good contrast sensitivity, and it is compared with the five existing approaches, such as, histogram equalization, adaptive histogram equalization, weighted histogram separation, adaptive weighted histogram separation and gray-level grouping.
Adaptive histogram equalization
Balanced histogram thresholding
Color normalization
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In this paper, we propose a method of region based histogram specification resulting dynamic range expansion effect. Main goal of this paper is enhancement of the image captured in back light condition, in which the captured image contains tow extremely contrasted regions simultaneously. The proposed method has two steps. The first is region segmentation in the spatial domain by using threshold values derived from the histogram domain. The second step is execution of independent histogram modification for the segmented regions. The histogram modification for each region is similar to typical histogram equalization except the interval to be histogram equalized. The resulting regionally histogram specified image can be obtained by combining the results of the previous steps. The proposed segmentation algorithm has an advantage in computational cost in that it can be performed with simple neighborhood operation by using the thresholding intensity level derived fast from histogram domina. The proposed algorithm shows better result than the typical histogram equalization by referring to specific region of interest to be enhanced.
Adaptive histogram equalization
Balanced histogram thresholding
Image histogram
Color normalization
Region growing
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We present a refinement of histogram equalization which uses both global and local information to remap the image grey levels. Local image properties, which we generally call neighborhood metrics, are used to subdivide histogram bins that would be otherwise indivisible using classical histogram equalization (HE). Choice of the metric influences how the bins are subdivided, affording the opportunity for additional contrast enhancement. We present experimental results for two specific neighborhood metrics and compare the results to classical histogram equalization and local histogram equalization (LHE). We find that our methods can provide an improvement in contrast enhancement versus HE, while avoiding undesirable over-enhancement that can occur with LHE and other methods. Moreover, the improvement over HE is achieved with only a small increase in computation time.
Adaptive histogram equalization
Color normalization
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Citations (49)
To address the technical shortcomings of conventional histogram equalization (HE), such as over-enhancement and artifacts, we propose a histogram-constrained and contrast-tunable HE technique for digital image enhancement. Firstly, the input image histogram is partitioned into two parts, the main histogram and the constrained histogram, by a cumulative probability density threshold; second, the main histogram is redistributed equally in the whole grayscale range; and finally, the nonlinearity of the constrained histogram is mapped to the main histogram. The experimental averages show that the values of the two metrics, information entropy and MS-SSIM, processed by the algorithms in this paper, are more accurate compared to the other six excellent algorithms.
Adaptive histogram equalization
Balanced histogram thresholding
Image histogram
Color normalization
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To overcome the problem that the histogram equalization can fail for discrete images, a local-mean based strict pixel ordering method has been proposed recently, although it is unpractical for 3D medical image enhancement due to its complex computation. In this paper, a novel histogram mapping method is proposed. It uses a fast local feature generation technique to establish a combined histogram that represents voxels' local means as well as grey levels. Different sections of the combined histogram, separated by individual peaks, are independently mapped into the target histogram scale under the constraint that the final overall histogram should be as uniform as possible. By using this method, the speed of histogram equalization is dramatically improved, and the satisfactory enhancement results are also achieved.
Adaptive histogram equalization
Balanced histogram thresholding
Image histogram
Color normalization
Feature (linguistics)
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In this paper, we modified a method known as self-adaptive plateau histogram equalization (SAPHE) to enhance microscopic images acquired using optical microscope. First, our method decides the plateau threshold value, automatically; based on the histogram itself. Then, using this plateau threshold, the bins of the histogram are modified. Finally, histogram equalization based on this modified histogram is carried out. Experimental results show that this modified SAPHE method is able to enhance the microscopic images without over amplifying the noise level, as compared with global histogram equalization.
Adaptive histogram equalization
Balanced histogram thresholding
Color normalization
Image histogram
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Wang and Ward developed an image contrast enhancement method called WTHE (Weighted and Thresholded Histogram Equalization). In this paper, we propose an improved variant of WTHE called DWTHE(Decomposable WTHE) that enhances WTHE through the use of histogram decomposition. Specifically, DWTHE divides an input histogram by using image's mean brightness or equally-spaced brightness points, computes a probability value for each sub-histogram, modifies the sub-histograms by using those probability values as weights, and then performs histogram equalization on the modified input histogram. As opposed to WTHE that uses a single weight, DWTHE uses multiple weights derived from histogram decomposition. Experimental results show that the proposed method outperforms the previous histogram equalization based methods.
Adaptive histogram equalization
Balanced histogram thresholding
Color normalization
Image histogram
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