本論文有兩大貢獻，分別為分類式濾波器群色彩濾波矩陣解馬賽克後補償技術與加速式參數導向區域直方圖等化技術，簡述如下：
分類式濾波器群色彩濾波矩陣解馬賽克後補償技術在本論文前半部進行說明，這個技術能夠進一步有效的改善既有解馬賽克技術的影像品質。此技術首先會將每個像素點根據其周圍的角度以及梯度強度來做分類，接著每個分類群組會各自利用最小均方演算法(Least-Mean-Square, LMS)去訓練其濾波器。從實驗數據中可以很明顯的看出，本論文所提出之技術可以大幅的提升影像品質，此外也有著較佳的視覺感。值得注意的是，本論文所提出之後補償架構可以有效的應用在任何前人所提出之解馬賽克法，來達到更佳的影像品質。
本論文後半部份介紹所提出的兩個區域對比強化技術，分別為參數導向直方圖等化(Parametric-Oriented Histogram Equalization, POHE)以及校準式參數導向直方圖等化(Correct Parametric-Oriented Histogram Equalization, CPOHE)技術，二者都能夠有效且準確的取得對比強化結果。基本上影像某區域的灰階分佈情形可以用特定的核心函數來塑造，如高斯函數(Gaussian function)，並利用其相對應的累積密度函數(Cumulative Density Function, CDF)當成用來做對比強化處理的轉換函數曲線來增強影像。然而其在核心函數相關參數計算上需要存取區域內所有像素點，造成許多額外的計算，為了解決此問題，本論文利用積分圖的概念(integral image)來化簡並加速參數的取得。為了更進一步改善區域對比，本論文所提出之CPOHE利用分類以及迴歸分析的方式來校準轉換函數以減少誤差。在實驗結果中，幾個過往有名的技術皆列入比較，且結果證明本技術在醫學影像和電腦視覺領域裡有高度的實行價值。

In this thesis, two contributions are delivered, including Classified-Filter-based Post-Compensation Scheme for Color Filter Array Demosaicing, and Speed-Up Parametric-Oriented Histogram Equalization.
In the first half, a classified-filter-based post-compensation scheme for color filter array (CFA) demosaicing is proposed. This technique can be used for improving the image quality of the interpolated result obtained by any former demosaicing method. First, each pixel is classified according to its neighborhood’s magnitude and angle. Then, different Least-Mean-Square (LMS) filters are trained for dealing pixels of various characteristics. As documented in the experimental results, the proposed scheme can substantially boost the image quality; in addition, a better visual perception can be obtained. Notably, the proposed method can be considered as effective post-compensation by applying for any former schemes to yield an even better image quality.
In the second half, two local contrast enhancement methods, namely Parametric-Oriented Histogram Equalization (POHE) and the Correct POHE (CPOHE), are proposed to effectively acquire the enhanced results while maintaining high accuracy on the contrast. The grayscale distribution of a specific region in an image can be modeled with a kernel function such as the Gaussian, thus the corresponding estimated cdf can be regarded the transformation function for contrast enhancement. However, the required parameters are still required by accessing all of the pixels, and thus consuming additional computations. To cope with this, the concept of integral image is adopted to effectively derive the required parameters. For further improving the local contrast, the distortion induced from the aforementioned cdf is analyzed, and it is further corrected by the proposed CPOHE through the concepts of classification and regression. In the experimental results, some former well-known methods are adopted for comparison, and it also demonstrates that the proposed methods provide high practical value for some active territories such as medical imaging and computer vision.

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