臺灣博碩士論文加值系統

本論文研究兩種影像處理的技術，包括了影像增強以及色彩轉換。隨著科技的進步，人們可以利用數位相機或是手機隨時拍攝生活周遭的畫面。然而這些隨手拍攝的影像，效果卻時常不如預期。為了讓影像更清晰、更漂亮，可以使用影像增強的技術來對影像做調整。影像增強的方法主要是調整對比以及銳利度，傳統增強對比的方法是整張影像做調整，這類型的方法雖然在過度曝光或是曝光不足的影像有較好的增強效果，但對於同時有暗部及亮部的影像會造成不理想的結果；局部性的對比增強可以適用於任何影像，使得影像的亮部、暗部都有更清晰的效果。在銳利度的增強方面，傳統的方法使用低通濾波器以及一般的加法和乘法做反銳化遮罩(Unsharp masking)，這種方法會造成光環效應以及亮度值超出範圍的問題；廣義的反銳化遮罩(Generalized unsharp masking)利用邊緣保持濾波器以及廣義的加法和乘法解決上述的問題。在影像增強方面，我們整理各種影像增強的技術，實作並比較這些方法。
另一方面，在合成影像或是電影後製處理的應用上，需要讓兩張或多張影像的色調一致，色彩轉換的技術在這類應用上扮演著很重要的角色，傳統的色彩轉換是以轉移統計數據而達到目的，當兩張影像相差太多的時候，這種方法會產生不自然的結果。基於直方圖匹配的色彩轉換，轉換後的結果在顏色上會非常接近目標影像，但是影像會出現顆粒效應。我們比較並分析兩種方法的優缺點，提出一個改良的色彩轉換方法，可以得到更自然的色彩轉換結果。此外我們將所提出的色彩轉換方法應用在特別為色盲者設計的影像重新著色，希望能讓色盲者可以欣賞到更漂亮的影像。最後我們也將所提出的色彩轉換方法應用在水下影像的增強，讓海洋工程的研究員可以得到更清晰的水下影像。

In this thesis, we study two image processing techniques including image enhancement and color transfer. With the development of technology, people can use digital camera or cell phone to capture the photo in daily life. However, the results of these easily captured images are often not as good as expected. In order to make images clear and pleasing, one can use image enhancement techniques to adjust images. Contrast correction and sharpness enhancement are the two common methods of image enhancement. Traditional contrast enhancement is global correction. Global correction provides good results for either overexposed or underexposed images. But it produces disappoint results for images which have both bright region and dark region. Local correction can make both bright region and dark region clearer. To enhance sharpness, traditional unsharp masking uses low pass filter and usual addition and multiplication. However, traditional unsharp masking suffers from halo artifact and out-of-range problem. A general unsharp masking adopts edge preserving filter and generalized addition and multiplication to resolve above problems. After studying kinds of image enhancement techniques, we implement and compare these methods.
In the applications of post-production industry and image composition, the color characteristic should be consistent between two or more images. Color transfer plays an important role in those of applications. Traditional method uses statistical matching to perform color transfer. When two images are too different, this method produces unnatural results. Histogram-based method improves the color similarity between the synthetic image and target image, but it suffers from grain artifact. We analyze and compare the methods as mentioned above, then provide an improvement of color transfer. Furthermore, we apply our color transfer method to perform image re-coloring for colorblindness. Hopefully to make a color-blind can enjoy more beautiful images. Finally, we apply our color transfer method to perform enhancement for underwater images. Ocean engineering researchers can get a clearer underwater image by our method.

口試委員會審定書 ..........................................#
誌謝 ....................................................i
中文摘要 .................................................iii
ABSTRACT ................................................v
CONTENTS ................................................vii
LIST OF FIGURES .........................................ix
LIST OF TABLES ..........................................xi
Chapter 1 Introduction ............................1
Chapter 2 Color image enhancement .................4
2.1 Histogram processing ............................4
2.1.1 Histogram equalization ..........................5
2.1.2 Histogram matching ..............................8
2.2 Gamma correction ................................11
2.3 Local contrast correction .......................15
2.4 Bilateral filter in local contrast correction ...18
2.5 Comparison between LCC and BFLCC ................25
2.6 Traditional unsharp masking .....................27
2.7 A generalized unsharp masking ...................31
Chapter 3 Color transfer ..........................40
3.1 Traditional method ..............................40
3.1.1 Color space conversion ..........................40
3.1.2 Statistical .....................................42
3.2 Histogram-based method ..........................45
3.2.1 Iterative distribution transfer algorithm .......45
3.2.2 Grain artifact ..................................49
3.2.3 Remove grain artifact ...........................52
3.3 Improvement of color transfer ...................54
3.4 Image quality assessment for color transfer .....58
3.5 Comparison and discussion .......................62
3.6 Image re-coloring for colorblindness ............68
3.7 Underwater image enhancement ....................74
Chapter 4 Conclusion and Future Work ..............78
4.1 Conclusion ......................................78
4.2 Future work .....................................79
Reference ...............................................80

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