由於顯示器的進步，⾼動態範圍的影像（ＨＤＲ）在最近變得越來越熱門，但是在高動態範圍影像的擷取上，專門用來擷取高動態範圍的相機對於⼤部份的
⼈來說都過於昂貴，因此有個反色調映射算法（ＩＴＭＯ），它是將低動態
範圍的影像利非線性或線性的曲線映射到⾼動態範圍去產生高動態範圍的
影像，雖然大部份的反色調映射算法可以產生不錯的⾼動態範圍影像，但是
仍然會產生一些問題，我們將問題分類成細節的損失問題和不自然的光暈問
題，在這篇論文，我們提出了局部亮源區域對比增強圖去解決亮度過飽和的
問題並保留細節資訊，我們結合兩張高斯模糊圖去產生我們提出的對比增強
圖，一張是為了做邊緣模糊，另一張是為了產生亮源區域的細節，我們使用
偽多重曝光反色調映射演算法去產生多張不同曝光的高動態範圍影像，分別
是最暗, 暗, 普通, 亮, 最亮，再利用不同曝光的影像去產生不同區域清晰的細
節，我們可以得到一張高品質的高動態影像，接著將最暗曝光的高動態影像
和高品質的高動態影像與對比
增強圖做結合，可以避免過飽和的問題並且清
楚的保留了亮源區域的細節。跟其他的方法比較，我們的方法在相似性上比
Pseudo-Multiple-Exposure HDR 方法好上1.7 ％，在視覺化差異量測75 ％和95
％分別比Pseudo-Multiple-Exposure HDR 方法好上67 ％和69 ％，在平均對比改
變錯誤上⽐Pseudo-Multiple-Expsure HDR 方法好上62 ％。

The High dynamic range (HDR) imaging becomes more and more popular recently
due to the advance of display devices. However, the acquisition of HDR images
from HDR cameras is expensive for most people. The HDR inverse tone mapping
(ITMO) is a technique, that maps low dynamic range (LDR) images to HDR
images. Although most ITMO method can produce good HDR images, however,
there are two problems in specular or light source regions of the HDR images:
loss of detail and unnatural Halo effects. In this thesis, we propose a contrast
enhanced expand map to reduce the over saturation problems for preserving the
details information. We fuse two Gaussian maps to generate an expand map. One
Gaussian map is for edge blurring, the other Gaussian map is to recover details
of the specular area. We adopt a pseudo multiple exposure ITMO to generate
multiple HDR images with different exposures. The darkest emulated HDR image
and the expand map are used to prevent saturation and reveal the details in the
specular areas. According the simulation results, our proposed method can reduce
the over saturation problem and reveal details in specular areas. It also reduces
the halo effects in light source areas. Comparing with Pseudo-Multiple-Exposure
HDR method, the proposed method has better average values in SSIM by 1.7 ％
. The proposed method has better average values in HDR-VDP-2 75 ％ and 95
％ by 67 ％ and 69 ％. The proposed method has better average values in mean
conrast error by 62 ％.

1 Introduction 1
1.1 Background of High dynamic range (HDR) . . . . . . . . . . . . . . 1
1.2 Motivation and Problem Description . . . . . . . . . . . . . . . . . 2
1.3 Goals and Contributions . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Related Works 6
2.1 Inverse Tone Mapping Methods . . . . . . . . . . . . . . . . . . . . 6
2.2 Pseudo-Multiple-Exposure HDR Processing (PMET) . . . . . . . . 8
2.3 Other expand map methods . . . . . . . . . . . . . . . . . . . . . . 14
3 HDR image with Local Specular Region Contrast Enhancement 18
3.1 Find specular area . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2 Specular region processing . . . . . . . . . . . . . . . . . . . . . . 20
3.3 Contrast Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . 24
4 Experimental Results 27
5 Conclusions 39
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

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