用在影像處理的高效模糊自動化對比增強法

在對比增強的方法中，影像模糊分群強化是屬於一種強化效果良好且全面性的方法，但是在模糊分群上常常因為需要多次迭代及群聚的測試來確保分群結果，但由於影像的尺寸隨著科技的進步而造成影像解析度增加，使得大量的像素在模糊分群需要耗費大量的運算時間，而本論文則是透過降低資料維度與使用機率密度函數（Probability Density Function: PDF）來提升群聚的分群速度及運算效率，並且與K-means的方群方法依照群聚準確度與運算時間比較，在影像增強方面，將RGB色彩空間轉換至L*a*b*色彩空間，並透過定義歸屬程度，來改變像素的強度，再依照影像亂度（影像熵）與影像可靠度作為影像增強的限制條件，使得本論文的對比強化方法能自動化地調整影像增強比例。並與傳統直方圖均勻化方法與強化效果良好的（Multi-Scale Retinex with Color Restoration; MSRCR）方法進行比較，並且針對不同影像色彩分布與不同尺度的影像進行實驗，依照均方根差、均方根對比及運算時間作為評比的標準，來凸顯本論文所提出的方法，其強化效果及運算效率皆有一定的水準。

關鍵字

機率密度函數；影像熵；自動化影像處理；對比強化

並列摘要

Fuzzy-Clustering enhancement is the most comprehensive approach in contrast enhancement for image processing. However, in order to keep an accurate cluster, Fuzzy-Clustering requires a great amount of computational effort in its iterative clustering process. With the advancement of vision science systems and technology, the image size is continuously growing and Fuzzy-Clustering is becoming more difficult to do in real time. In this thesis, a method utilizing the Probability Density Function (PDF) is proposed to reduce the data dimension in identifying clusters. The advantage of using the PDF removes the need for iterations and trials in the clustering process. This method is also compared to the traditional with K-means clustering. The efficiency and cluster accuracy are then compared. In the image data processing, each pixel is identified in the L* a* b* color space instead of the RGB color space. An image entropy function is then defined. By identifying the maxima of the image entropy, it is claimed that the image enhancement procedure is made automated and at the same time adequate contrast enhancement is reliably achieved. The method is then compared and tested with the Histogram Equalization method (HE) and Multi-Scale Retinex with Color Restoration (MSRCR). The results show that the proposed method, in low contrast images, performs superior in terms of its contrast enhancement and is very promising for high-resolution images. Keywords: Probability Density Function, image entropy, automatic image processing

並列關鍵字

Probability Density Function ； image entropy ； automatic image processing ； Contrast Enhancement

參考文獻

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國際替代計量

用在影像處理的高效模糊自動化對比增強法

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