本論文提出自適性參數導向直方圖等化(Adaptive Parametric-Oriented Histogram Equalization, APOHE)來有效率地產生區域對比增強影像，首先，利用多個高斯分佈來預測參考範圍內的灰階直方圖分佈，其中預估的精準度可由兩個可調的參數(α,β)來控制，藉此得到良好的對比，當中為了提升處理效率，建立高斯分佈所需的平均值及標準差可透過積分圖的概念來快速取得，同時為了更進一步改善對比，於是提出校準式自適性參數導向直方圖等化(Adaptively corrected Parametric-Oriented Histogram Equalization, AcPOHE)來針對對比做補償，實驗結果顯示本論文提出的方法具有不錯的實用價值，因此可應用到不同的領域，例如圖案辨識、生物分析系統和監控系統，另外，比起先前的區域式對比強化技術，本論文提出的方法可以同時提供高對比且無不自然現象的增強結果。
雖然區域式對比強化技術能夠提供豐富的影像細節，但同時也會強化影像中存在的雜訊，尤其在灰階變化平緩的區域特別明顯，因此提出對比限制自適性直方圖等化(Contrast Limited Adaptive Histogram Equalization, CLAHE)來解決此問題，它藉由限制轉移函式的斜率，來有效抑制雜訊的增強，但龐大的運算量一直是CLAHE的主要問題，為了解決這問題，本論文提出積分對比限制自適性直方圖等化(Integral CLAHE, ICLAHE)，透過積分影像的概念以及機率密度函數(PDF)裁切的過程來有效降低運算量，且強化結果與CLAHE完全一致，最後與近幾年的區域對比強化技術相比，ICLAHE不僅僅是最簡單的方法讓增強影像有良好的雜訊免疫能力與較少的邊緣暈開，同時也提供明顯的紋理細節，因此顯示ICLAHE仍具有很大的優勢在醫療成像領域。

In this thesis, Two regional contrast enhancement schemes are proposed. The first one, termed adaptive parametric-oriented histogram equalization (APOHE), is proposed to effectively generate artifact-free regional contrast enhanced images. First, the grayscale histogram of a specific region is modeled with multiple Gaussian distributions adjusted by two user-defined parameters (α,β) for yielding good contrast. In which, to improve processing efficiency, the required mean and variance of these Gaussian distributions can be rapidly derived through the concept of integral image. In addition, the adaptively corrected POHE (AcPOHE) is also proposed to further improve the contrast with a limited trade-off on computations. Experimental results demonstrate good practical values of the proposed method, and thus it can be applied for various applications such as pattern recognition, biometrics analysis system and surveillance system. Comparing with former speed-oriented methods, good contrast and artifact-free results can be achieved simultaneously.
Although regional contrast enhancement methods can obtain richer details as expected, the noises accompanied with the images are enhanced as well, in particular those homogeneous regions. To solve this issue, the contrast limited adaptive histogram equalization (CLAHE) is proposed. The method utilizes the AHE structure with restricted slope of the transformation function for the reduction of noises. Yet, massive computational complexity is its major deficiency. To cope with this, a method termed integral CLAHE (ICLAHE) is proposed to specifically address this issue. In this method, the concept of integral image and the property during pdf clipping are both exploited for less computations from the original O(M^2×P^2) to O((L+1)×P^2) for images of size P×Pand contextual region size M×M. Compared with state-of-the-arts regional contrast enhancement methods, the proposed method is not merely the simplest method of providing less halo effect and noises, but offering richer distinguishable textual details. As a result, a great potential of the proposed method on medical imaging is demonstrated.

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