臺灣博碩士論文加值系統

影像增強可有效的提升影像的清晰度，能夠清楚地反映被拍攝景物的明亮度和細微的顏色差別。最常見的影像增強方法是使用灰階直方圖調整。傳統直方圖等化法為最普遍的灰階直方圖調整方法，乃是利用累加機率值有效率地拉開直方圖灰階的間距，但輸出的部份影像會產生亮度失真及喪失原始影像資訊。為了改善其缺點，已有許多學者提出區段性影像對比增強演算法，主要是以原始影像直方圖波峰、波谷值之特性或是固定平均的方式做切割，而這些方法可以改善直方圖等化所產生的缺點，但僅能適用於特定影像增強。
因此，本論文提出建立於以人類視覺特性為基礎之影像增強演算法，有效地改善直方圖等化所造成的缺點，且清楚將影像中細節給增強顯現出來，並同時保留原本影像中對比清楚的亮部細節資訊。實驗結果與以往研究之演算法互相比較，驗證視覺性影像增強演算法(VHE)的強健性及增強後的視覺品質及效能。

Image enhancement can effectively enhance the clarity of the image, and clearly reflect the brightness and subtle color differences of the shooting scene. The most common image enhancement method is gray-scale histogram adjustment. The traditional histogram equalization method is the most widely used technique in gray-scale histogram adjustment, and it utilizes the cumulative probability value to effectively pull the gray scale spacing of histogram. However, a part of the export image is brightness distortion and loss of original information. In order to improve its shortcomings, many researchers have proposed the section of the image contrast enhancement algorithms. It is mainly based on the histogram of the peaks and troughs value or fixed average way to do the cutting. Although these methods avoid the shortcomings of the histogram equalization, they only apply to the enhancement of specific image.
This paper proposed the establishment of the image enhancement algorithm based on the human visual system. The method that we have developed can be effectively improved the disadvantages of the histogram equalization, enhance the clarity of the image details and retain the bright detailed information of the original image. The experimental results compare with the algorithm of the previous studies, verify that the visual images enhance the robustness of the algorithms (VHE) and enhanced visual quality and performance.

目錄

摘要 iv
ABSTRACT v
目錄 vi
表目錄 viii
圖目錄 ix
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 6
2. 研究現況探討 7
2.1. 影像增強之研究現況 7
2.2. 相關重要研究結果分析 8
3. 人類視覺JND特性 20
4. 視覺化影像增強 24
4.1. 視覺化影像增強流程介紹 24
4.2. JND對比調整 25
4.3. 還原壓縮灰階值 28
4.4. 邊緣偵測 30
4.5. 篩選局部細節特徵 31
4.6. 局部細節特徵增強 33
5. 實驗結果與討論 34
5.1. JND對比調整影像 36
5.2. 視覺性影像增強 41
6. 結論與未來展望 55
6.1. 結論 55
6.2. 未來展望 55
參考文獻 56

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