臺灣博碩士論文加值系統

本研究提出一種建立於適應性切割的影像增強演算法。在本論文中，適應性直方圖切割法，利用統計分析中聚類變異量的變化關係，由直方圖分布找出適當的切割點，分割影像至各不同的物件層面。經過分割後，不同的物件和背景被分割成單獨的群集，稱為物件層面。得到物件層面的分佈後，根據視覺特徵調整每個物件的動態範圍。最後每個物件層面各別在新的動態範圍以直方圖等化增強法增強。由於適應性切割能自動分割圖像成不同的物件層面，提高了影像的視覺特徵，根據物件層面，每個物件和背景元件的影像可以很有效地被增強。實驗結果採用對比度較低的影像與以往研究之演算法互相比較，驗證適應性切割影像增強演算法的強健性，增強後的視覺品質以及效能。

An image enhancement algorithm based on adaptive segmentation for image contrast enhancement is presented. In this study, an automatic adaptive segmentation histogram enhancement (ASHE), based on discriminant analysis, is utilized to recursively segment an image into several clusters first. After segmentation, different object and background components are segmented into separate clusters, called object planes. Then, the dynamic range of each object plane is adjusted according to its visual characteristics. Finally, each object plane is enhanced within the new dynamic range respectively. Because the proposed algorithm can automatically segment an image into different object planes and enhance the image according to the visual characteristic of each object plane, each object and background components of the image can be well enhanced. Experimental results for poor-contrast images and the comparisons for some of the previous studies are provided to demonstrate the robustness, visual quality, and effectiveness of the proposed algorithm.

目錄

1. 緒論 1
1.1. 研究動機與目的 1
1.2. 文獻探討 2
1.3. 論文架構 7
2. 研究現況探討 8
2.1. 影像增強之研究現況 8
2.2. 相關重要研究 9
3. 適應性影像增強 19
3.1. 適應性切割法 20
3.2. 物件特徵範圍分配 23
3.3. 直方圖等化 25
4. 實驗結果與討論 27
4.1. 實驗軟體介面以及實現環境 27
4.2. 適應性切割方法 28
4.3. 適應性影像增強 41
5. 結論與未來展望 55
5.1. 結論 55
5.2. 未來展望 55

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