在現今數位化的年代，消費型相機通常人手一台，因此人們對於數位影像的需求愈來愈大，而設備也愈來愈專業。控制數位影像拍攝品質有三大因素：光圈、快門及感光度。光圈用來調整進光孔的大小，光圈愈大，瞬時進光量就愈多；
快門則用來控制曝光的時間，時間的長短通常取決於拍攝的人員；感光度的數值愈大，表示對光的敏感度愈高。這三個重要的因素，只要有任何一個出錯，就有可能造成影像呈現過暗或過亮的情況。簡單的來說，這三個參數可用於控制影像亮度，
而亮度也是人眼最直覺決定影像好壞的第一要件。

本論文提出了基於分區系統之影像對比強化演算法。在此演算法中，我們以分區系統為基礎，首先對於影像進行初步的分類，將影像分成過暗、適中及過亮三種情況，並依據不同的類別影像設計不同的全域亮度轉換函數。
此轉換函數可針對過暗像素提升亮度使得暗部細節呈現，並且將過亮像素降低亮度以避免過度曝光且呈現出當中的細節。經由亮度調整後的影像，影像的視覺品質獲得了改善，隱藏細節的呈現也有了不錯的效果。
但是在於對比度的呈現上，效果並不太理想，因此我們加入了對比強化的方法。

在本論文中，我們另外提出了一個以直方圖量化為基礎的改善方法，主要是為了避免其過度強化、失真、失去細節及影像不自然等缺點。做法是以固定個數對直方圖進行切割得到其不同的範圍值，在愈密集的區域得到的範圍值就愈小，
反之於愈稀疏的區域得到的範圍值則愈大，最後定義出一個新的範圍分佈函數。不同於直方圖量化著重於密集區映射到較大的範圍區間而壓縮了稀疏區的資訊，我們用範圍分佈函數來修正原直方圖的機率密度函數再進行直方圖的量化。
經由對比強化處理後的影像，在對比度的呈現上，均有顯著的提升，並且在改善視覺品質上有不錯的成效，但是整體有過暗的情況。

因此我們提出第三個方法，結合上述的兩個方法，先調整亮度，後調整對比度。經由處理後的影像，解決了前兩個方法的缺點，並且結合了其優點，在對比度及細節的呈現上有顯著的提升。

經由本論文提出的三個方法，其中我們針對影像調整到適當的亮度，再進行對比度的強化，同時我們使用不同類型的影像進行測試都有不錯的效果。

Digital imaging devices are becoming increasingly specialized. The three factors that determine the exposure of images are the aperture of the lens, the shutter speed, and ISO. The aperture adjusts the size of the opening through which light reaches the sensor. In sophisticated cameras, this can be adjusted; however, in many devices it is a fixed value determined by the lens designer. The shutter determines how long light will be able to pass through to the sensor, and is commonly selected by the user. The ISO indicates the sensitivity for the sensor to light. Unless these three factors are combined correctly, the result will appear too bright or too dark. In simple terms, these three parameters are used to control the brightness of the image and brightness is the single most important factor in the determination of image quality.

This thesis proposes an image contrast enhancement algorithm, which uses the Zone System to classify images into three categories: too dark (low-key), too bright (high-key), and moderate (middle-key). Exposure and contrast adjustment is then applied according to the category to which the image belongs. A conversion function enhances the brightness of dark pixels in order to extract detail from dark areas. At the same time, the bright pixels are prevented from exceeding an acceptable exposure threshold. This method improves the visual quality of the image and reveals hidden details; however, the effect on contrast is not always ideal. Thus, we developed a second step, applying exposure compensation in conjunction with contrast enhancement.

We modified the standard approach to histogram equalization, in order to avoid excessive enhancement, distortion, loss of detail, and rendering that appears unnatural. Values representing the range of tones are obtained by dividing the histogram using a fixed number. The dense areas of the histogram are assigned a low value and vice versa. The value of this range is then used to define a new range distribution function. Thus, histogram equalization can be selectively applied to dense areas by mapping them to cover a wider range, while compression is applied to areas with sparse information. The range of the distribution function is used to correct the probability density function of the original histogram prior to histogram equalization. In this manner, the contrast and details can be altered to improve image quality.

The adjustment of luminance in conjunction with contrast enhancement produced excellent results in images covering a diversity of subjects across a wide range of exposures and lighting scenarios.

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