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研究生: 邱檉文
Chiu, Cheng-Wen
論文名稱: 模擬多重曝光及區域調整之色調合成
Pseudo-Multiple-Exposure based Tone Fusion with Local Region Adjustment
指導教授: 邱瀞德
Chiu, Ching-Te
口試委員: 范倫達
Van, Lan-Da
黃志煒
Huang, Chih-Wei
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年: 2012
畢業學年度: 100
語文別: 英文
論文頁數: 39
中文關鍵詞: 高動態範圍低動態範圍反色調對映區域加強對比加強
外文關鍵詞: High Dynamic Range (HDR), Low Dynamic Range (LDR), Local Region Enhancement
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  • 随著顯示器技術的進展,顯示設備已具有更高的動態顯示範圍,因應此趨勢利用反色調對映運算(inverse tone mapping operator)可將亮度擴展至高動態顯示範圍,而能夠得到接近於真實世界的影像。
    目前主要有兩種可以獲得高動態範圍影像的方法,其中一種是利用單張圖片
    ,透過反色調對映運算去調整圖片中的色調以獲得高動態範圍影像。另外一種方法是將多張具有相同場景但不同曝光程度的圖片,透過權重值的挑選,挑選出圖片中曝光度較為合適的區塊後加以融合,已取得高動態範圍圖片。
    真實世界動態範圍的影像、圖片取得困難,因為需要多張不同曝光圖片,因此我們提出一個可調整曝光程度的S曲線將單張低動態範圍圖片產生多張不同曝光程度的圖片,再由高斯權重函式合成為一張高動態範圍圖片。根據不同亮度的區域,給予的權重也有所不同,而可以讓合成出的圖片在最亮和最暗區域的細節能夠顯現出來。
    實驗結果顯示和其它反色調對映運算相比,我們的方法有較高的區域和全域對比度,在較亮區域的細節也比其它方法來的明顯,而我們的方法也很適合用在高動態範圍的視訊。


    New generations of display technologies provide significantly improved dynamic range over conventional display devices. Inverse tone mapping are proposed to convert low dynamic range (LDR) images to HDR ones but they generally require multiple exposure LDR images of the same scene as inputs. However, the vast majority of LDR images and videos available have only one single exposure.
    We propose an exposure dependent S curve to convert one normal exposure LDR image to multiple images with different brightness. The S curve enhances the mid-tone regions, avoids saturation in bright regions and boosts intensity in dark regions of an image. In addition, we propose the exposure and region dependent Gaussian weighting functions to fuse multiple images with different brightness. The fused image has enhanced detail in the bright region of the dark image and dark region of the bright image.
    According to our implementation results, the dynamic range can reach from 0.0001~100. Compared with other inverse tone mapped images, our results have lower visual difference and higher contrast measure. The emulated multiple exposure tone fusion method can be applied to LDR images to preserve and enhance details of the image.

    1 Introduction 5 1.1 Recent Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2 HDR-based Pseudo-Multiple-Exposure Tone Fusion (HPMET) 9 2.1 Inverse Tone Mapping Function . . . . . . . . . . . . . . . . . . . . 10 2.2 Generation of Emulated Images with Multiple Exposures . . . . . . 11 2.3 Local Region Segmentation . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Local Region Enhancement . . . . . . . . . . . . . . . . . . . . . . 15 2.5 Tone Fusion of Emulated Images with Multiple Exposures . . . . . 19 3 LDR-based Pseudo-Multiple-Exposure Tone Fusion (LPMET) 25 4 Results and Performance Analysis 29 4.1 RMS Contrast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2 Relative Entropy Contrast . . . . . . . . . . . . . . . . . . . . . . . 30 5 Conclusion 35

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