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研究生:丁榮豐
研究生(外文):Jung-Feng Ting
論文名稱:基於區塊的亮度特性及熵值合併多張不同曝光值影像之高動態範圍影像生成法
論文名稱(外文):Based on Block Intensity Characteristic and Entropy Synthesized Differently Exposed Images for High Dynamic Range Images Generation
指導教授:何裕琨
指導教授(外文):Yu-Kuen Ho
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:63
中文關鍵詞:影像合併高動態範圍影像高斯混合函數熵值
外文關鍵詞:entropyGassian Blending Functionimage mergeHigh dynamic range image (HDRI)
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  • 被引用被引用:4
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一般數位相機所拍攝出的靜態影像,往往無法確實重現人眼當時所看到的真實景象。這是因為受限於單一曝光值影像其所能表達的動態範圍(Dynamic Range)遠低於真實景象的動態範圍。為了解決這個問題,利用多張不同曝光值影像之演算法應運而生。此類演算法是合併多張不同曝光值之影像來生成一張高動態範圍影像(High Dynamic Range Image, HDRI),以解決因單一曝光值影像動態範圍過小而產生色彩漸層遠低於人眼所見之問題。
由A. R. Várkonyi-Kóczy等學者所提出以梯度為基礎合併多張不同曝光值影像的方法,是藉由將影像分成若干個相同大小的區塊,從多張不同曝光值影像同位置之區塊中挑選適當的曝光值區塊合併成一張高動態範圍影像。基於利用多張不同曝光值影像可合成高動態範圍影像之原理,本論文提出了一個以區塊的亮度特性及熵值為基礎合併多張不同曝光值影像之高動態範圍影像生成法,藉由對區塊計算其亮度特性及熵值,使的挑選到的區塊色彩漸層較豐富的目的。因為此一高動態範圍影像是由所選取區塊之合成,因此合併的影像在區塊與區塊相鄰交接處會出現亮度色差之現象,為了解決這個問題,本論文使用高斯混合函數(Gassian Blending Function)來消除區塊與區塊相鄰接處之亮度色差,以產生一張高品質之高動態範圍影像。
經過實驗與統計分析,本論文所提出之演算法能夠有效地合併多張不同曝光值影像來產生一張高動態範圍影像。目視比較產生之影像較亮以及較暗處的細節與色彩漸層皆有良好的表現,並且保有原始影像的視覺觀感。
本論文並以常用的多張影像RGB畫素平均法以及一個市售影像合併軟體作比較,由合成影像之亮度直方圖得知,在亮部以及暗部明顯比多張影像RGB畫素平均法所能呈現的細節優良許多;而與市售影像合併軟體相比,在亮度直方圖上得知,市售軟體所形成的影像亮度上整體偏亮。因此可證明在色彩漸層上經由本論文所提出之演算法產生的影像有較好的視覺表現。
When the average digital camera takes a still image, it can not thoroughly re-create what was seen with the human eyes. This is due to the dynamic range of a single exposure images are much lower than that of the actual scenery. In order to resolve this problem, we can utilize the method of synthesizing several images of different exposures. With this method of combination, we can then create a high dynamic range image (HDRI) in order to overcome the limitations of singular exposure.
A. R. Várkonyi-Kóczy and others propose a gradient based synthesized multiple exposure time HDR image. Among the same grided picture with differnt expore settings, several appropriated blocks from the different grides with the same exposure range would be selected and merged to form a HRDI. From the basis of combining several exposing images into one, we propose a based on block intensity characteristic and entropy synthesized differently exposed images for high dynamic range images generation. From calculating the block intensity characteristic and entropy, we can then pick out better ranges of gradient levels to form a better image. Because the HDRI was formed from the selected blocks and portions, therefore one may detect discrepancy among other blocks or portions of the same image. In order to solve this problem, we utilize the Gassian Blending Function to eliminate the variances of every block to form a good quality image.
From tests and analysis, we find this function can effectively combine good quality HDRI. From examining through the naked eye, we can see the bright and dark details now have a better distribution of brightness, and also preserved its original essence.
In our research, we used the common average RGB pixel method and also image processing software we bought off the shelf. From the image histogram, we found that the details of bright and dark areas are much better than average RGB pixel method. And when compared to the image processing softwares, we can see that the results are usually brighter than reality. Thus, we have demonstrated that this function method is a good way to enhance an image.
目錄
第一章 緒論............................................1
第二章 相關背景........................................8
2.1 HSV色彩空間格式.................................8
2.2 自動曝光值演算法...............................10
2.3 直方圖等化法...................................21
2.4 融合多張原始影像與色調再生的高動態範圍影像.....24
2.5 以梯度為基礎合併多張不同曝光值影像生成一張高動態
範圍影像.......................................26
2.6 高斯混合函數...................................28
第三章 基於區塊的亮度特性及熵值合併多張不同曝光值影像之高動態範圍影像生成法...........................30
3.1 影像切割.......................................32
3.2 區塊亮度特性及熵值運算.........................34
3.3 合併區塊.......................................36
第四章 實驗結果.......................................44
4.1 實驗相關配備及環境.............................44
4.2 有無熵值運算對輸出影像品質的比較...............45
4.3 切割區塊大小對輸出影像品質的關係...............48
4.4 與其他方法之比較...............................50
4.5 其他不同曝光值影像合併之結果...................53
第五章 結論與未來展望.................................60
參考文獻 ...............................................61
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