這篇論文提出了一個對比強化演算法。有些演算法只有全域性或區域性的資訊來強化，但是這樣常常會造成過度強化而使影像看起來不自然，而提出的演算法則結合這兩種資訊。在全域性的部分，經由對應曲線來找出新的平均亮度、最大及最小亮度，以求符合人眼視覺系統(Human Visual System, HVS)概念。而在區域性的部分，則利用fuzzy c-means分群演算法對影像分群，而每一群都可以得到亮度分布及像素數目這兩種資訊，再藉由這兩種資訊計算出權重，並基於這些權重以直方圖等化的方式來強化影像。
實驗結果顯示，我們的演算法可以穩定地強化影像的對比，而且會有較小的機率產生過度強化的情況，讓整體影像看起來自然且可以清楚地呈現出更多細節。

This thesis proposed a contrast enhancement algorithm. Some methods enhance images depending on only the global or the local information, therefore it would cause over-enhancement usually and make the image look unnatural. The proposed method enhances image based on the global and local information. For the global part, we proposed mapping curves to find the new average, maximum, and minimum intensity to try to suit the concept of Human Visual System (HVS) for obtaining the better perceptual results. For the local part, we utilized fuzzy c-means clustering algorithm to group image, and we can obtain the information of intensity distribution and pixel number from each group. Then we calculate weights according to the information, and enhance images by histogram equalization (HE) depending on the weights.
The experiment results show that our method can enhance the contrast of image steadily, and it causes over-enhancement with lower probability than other methods. The whole image not only looks natural but also shows detail texture more clearly after applying our method.

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