在對比增強的這塊領域中，許多主要的方法是將柱狀圖切分成許多的子區域來做柱狀圖等化法。在此論文中提出在柱狀圖中用混合高斯模型來做新的對比增強法。我們的方法中提出了五個主要的步驟，第一步，我們利用成本方程式計算出混合高斯模型需要幾個群組，計算出群組參數主要是於圖片的柱狀圖中能夠用混合高斯模型來逼近，之後使用二分搜尋法找出彼此兩個鄰近高斯的交點，針對柱狀圖的各個子區域做傳統的柱狀圖等化法來達成對比增強，最後利用保亮度機制將整張圖片做處理。在論文中利用三張實驗結果圖證明此方法優於十二種方法。

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.

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