Gaussian mixture modeling of histograms for contrast enhancement
| Autor: | Chyouhwa Chen, Guei-Yin Lin, Yu-Ren Lai, Kuo-Liang Chung |
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| Rok vydání: | 2012 |
| Předmět: |
Balanced histogram thresholding
business.industry Gaussian ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION General Engineering k-means clustering Histogram matching Pattern recognition Mixture model Computer Science Applications symbols.namesake Artificial Intelligence Computer Science::Computer Vision and Pattern Recognition Histogram symbols Adaptive histogram equalization Artificial intelligence business Histogram equalization Mathematics |
| Zdroj: | Expert Systems with Applications. 39:6720-6728 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2011.12.018 |
| Popis: | The current major theme in contrast enhancement is to partition the input histogram into multiple sub-histograms before final equalization of each sub-histogram is performed. This paper presents a novel contrast enhancement method based on Gaussian mixture modeling of image histograms, which provides a sound theoretical underpinning of the partitioning process. Our method comprises five major steps. First, the number of Gaussian functions to be used in the model is determined using a cost function of input histogram partitioning. Then the parameters of a Gaussian mixture model are estimated to find the best fit to the input histogram under a threshold. A binary search strategy is then applied to find the intersection points between the Gaussian functions. The intersection points thus found are used to partition the input histogram into a new set of sub-histograms, on which the classical histogram equalization (HE) is performed. Finally, a brightness preservation operation is performed to adjust the histogram produced in the previous step into a final one. Based on three representative test images, the experimental results demonstrate the contrast enhancement advantage of the proposed method when compared to twelve state-of-the-art methods in the literature. |
| Databáze: | OpenAIRE |
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