Contrast enhancement based on a novel homogeneity measurement

Cheng, Heng-Da; Mei, Xue; Shi, Xiangjun

doi:10.1016/s0031-3203(03)00054-2

Cited by 62 publications

(71 citation statements)

References 12 publications

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“…Many procedures for contrast enhancement such as Histogram Equalisation have been proposed, especially for aviation, medical digital radiography and infrared imaging systems where the initial images are of poor quality [4]. This work presents a contrast enhancement algorithm that preserves the colour of an image.…”

Section: F (X Y) = I(x Y) × R (X Y)mentioning

confidence: 99%

Colour-Preserving Contrast Enhancement Algorithm for Images

Ojo

Solomon

Adeniran

2016

Emerging Trends and Advanced Technologies for Computational Intelligence

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Conventional contrast enhancement techniques often fail to produce satisfactory results for low-contrast images, and cannot be automatically applied to different images because their processing parameters must be specified manually to produce a satisfactory result for a given image. This work presents a colourpreserving contrast enhancement (CPCE) algorithm for images. Modification to images was performed in the HSV colour-space. The Hue component is preserved (unchanged), luminance modified using Contrast Limited Adaptive Histogram Equalization (CLAHE), while Saturation components were up-scaled using a derived mapping function on the approximate components of its discrete wavelet transform. Implementation was done in MATLAB and compared with CLAHE and Histogram Equalization (HE) algorithms in the RGB colour space. Subjective (visual quality inspection) and objective parameters (Peak-signal-to-noise ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Mean squared error (MSE)) were used for performance evaluation. The method produced images with the lowest MSE, AMBE, and highest PSNR when tested, yet preserved the visual quality of the image.

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Section: F (X Y) = I(x Y) × R (X Y)mentioning

confidence: 99%

Colour-Preserving Contrast Enhancement Algorithm for Images

Ojo

Solomon

Adeniran

2016

Emerging Trends and Advanced Technologies for Computational Intelligence

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“…20 and Ref. 21 is carried out to combine regions that are similar to each other. After this step, the final segmented image is generated.…”

Section: Image Segmentationmentioning

confidence: 99%

A Novel Neutrosophic Logic SVM (N-Svm) and Its Application to Image Categorization

Cheng

2013

New Math. and Nat. Computation

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Neutrosophic logic is a relatively new logic that is a generalization of fuzzy logic. In this paper, for the first time, neutrosophic logic is applied to the field of classifiers where a support vector machine (SVM) is adopted as the example to validate its feasibility and effectiveness. The proposed neutrosophic set is integrated into a reformulated SVM, and the performance of the obtained classifier N-SVM is evaluated under a region-based image categorization system. Images are first segmented by a hierarchical two-stage self-organizing map (HSOM), using color and texture features. A novel approach is proposed to select the training samples of HSOM based on homogeneity properties. A diverse density support vector machine (DD-SVM) framework is then applied to viewing an image as a bag of instances corresponding to the regions obtained from image segmentation. Each bag is mapped to a point in the new bag space, and the categorization is transformed to a classification problem. Then, the proposed N-SVM is used as the classifier in the new bag space. N-SVM treats samples differently according to the weighting function, and it helps to reduce the effects of outliers. Experimental results have demonstrated the validity and effectiveness of the proposed method which may find wide applications in the related areas.

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“…3(e) shows how is minimized when the mixture model is being trained. The threshold that separates soft tissue from bone structures, , can be set so that the following function is minimized: (18) that is, the probability of assigning to the wrong component is minimized, for or . Finally, the greatest significant GL of the image, , can then be identified as (19) where and are the mean and the standard deviations, respectively, of the inverted lognormal distribution of the mixture model.…”

Section: Mixture Model For Histogram Based Clusteringmentioning

confidence: 99%

“…Solutions based on local statistics, such as local histogram equalization [13], [16], [17] or homogeneity analysis [18], [19], reframe the task as a globally constrained nonlinear optimization problem, where the remapping of GLs is constrained at different thresholds while maintaining the same ordering. These solutions have the drawback of being computationally intensive and may suffer from over-enhancement.…”

mentioning

confidence: 99%

Enhancing digital cephalic radiography with mixture models and local gamma correction

Frosio

Ferrigno²,

Borghese³

2006

IEEE Trans. Med. Imaging

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Abstract-We present a new algorithm, called the soft-tissue filter, that can make both soft and bone tissue clearly visible in digital cephalic radiographies under a wide range of exposures. It uses a mixture model made up of two Gaussian distributions and one inverted lognormal distribution to analyze the image histogram. The image is clustered in three parts: background, soft tissue, and bone using this model. Improvement in the visibility of both structures is achieved through a local transformation based on gamma correction, stretching, and saturation, which is applied using different parameters for bone and soft-tissue pixels. A processing time of 1 s for 5 Mpixel images allows the filter to operate in real time. Although the default value of the filter parameters is adequate for most images, real-time operation allows adjustment to recover under-and overexposed images or to obtain the best quality subjectively. The filter was extensively clinically tested: quantitative and qualitative results are reported here.

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Contrast enhancement based on a novel homogeneity measurement

Cited by 62 publications

References 12 publications

Colour-Preserving Contrast Enhancement Algorithm for Images

Colour-Preserving Contrast Enhancement Algorithm for Images

A Novel Neutrosophic Logic SVM (N-Svm) and Its Application to Image Categorization

Enhancing digital cephalic radiography with mixture models and local gamma correction

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