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研究生:張維哲
研究生(外文):Wei-zhe Chang
論文名稱:立體顯影之高動態範圍影像合成
論文名稱(外文):STEREOSCOPIC HIGH DYNAMIC RANGE IMAGING
指導教授:林惠勇
指導教授(外文):Huei-Yung Lin
學位類別:碩士
校院名稱:國立中正大學
系所名稱:電機工程所
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:2008
畢業學年度:97
語文別:中文
論文頁數:75
中文關鍵詞:立體高動態範圍影像鬼影
外文關鍵詞:stereohigh dynamic range imageghost
相關次數:
  • 被引用被引用:2
  • 點閱點閱:399
  • 評分評分:
  • 下載下載:52
  • 收藏至我的研究室書目清單書目收藏:2
影像的動態範圍(dynamic range)指的是影像中最亮與最暗區域的對比率,傳統的影像擷取及顯示裝置因為動態範圍不足,無法真實的表現出明暗對比較大的影像。因此,有學者提出了以多張曝光度不同的影像,來合成出高動態範圍影像(high dynamic range image)的方法。然而,使用一台相機拍攝不同曝光度的影像,無法在同一時間擷取多張曝光度不同的影像,因此在拍攝動態景物的時候,會產生前後影像不一致的問題。本論文提出了使用兩台相機同時拍攝影像的方法,我們利用兩張影像的特徵點對應,來推算相機反應函數(camera response function),並透過對應點搜尋演算法,使兩張影像能重合在一起,並同時擷取影像深度資訊,增加高動態範圍影像的可應用性。
Dynamic range is the ratio between the brightest and darkest intensity of an image. Traditional image acquisition and display devices do not have enough dynamic range to display the large dynamic range image of a real-world scene. Several researchers have presented a method to produce a high dynamic range image from a set of photographs taken with multiple exposures. However, one camera can not capture many different exposures at the same time. If some object was moving during image sequence acquisition, it may appear as ghost in the synthesized. This thesis propose a new method that uses two cameras to capture different exposures image at the same time of high dynamic range image synthesis. We find the corresponding points of the two images to calculate the camera response function. And then we use an algorithm for searching corresponding points to match the two images and capture the depth information of images for more dynamic range image applications.
摘要
Abstract
誌謝
圖目錄
表目錄
中英字對照
1.緒論
1.1 研究動機
1.2 相關研究及應用
1.3 論文架構
2.基礎相機理論與立體影像
2.1 相機模型
2.2 影像深度資訊
2.2.1 三角測量法
2.2.2 對應點搜尋
3. 高動態範圍影像理論
3.1 影像的曝光度與動態範圍
3.1.1 光圈、快門與曝光量
3.1.2 影像的動態範圍
3.2 相機反應函數
3.3 建立高動態範圍影像
3.4 鬼影消除
4.多相機高動態範圍影像合成系統
4.1 系統流程
4.2 特徵點匹配
4.3 推算相機反應函數
4.4 影像正規化
4.5 對應點搜尋
4.6 高動態範圍影像合成
4.6.1 殘影去除
4.6.2 高動態範圍影像格式
4.7 色調對應
4.7.1 簡介色調對應技術
4.7.2 Reinhard所提出之色調對應方法
4.7.3 Drago所提出之色調對應方法
5.環境架設與實驗結果
5.1 測試影像結果
5.1.1 實驗一:測試影像art
5.1.2 實驗二:測試影像doll
5.1.3 測試影像結果比較與分析
5.2 實際影像結果
5.2.1 環境架設與實驗器材
5.2.2 實驗三:實際影像
5.2.3 實驗四:實際影片
5.2.4 實際影像結果分析
6.結論與未來展望
參考文獻
[1] P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” Proc. SIGGRAPH, pp. 369-378, 1997.
[2] T. Mitsunaga and S.K. Nayar, “Radiometric self calibration,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 374-380, 1999.
[3] T. Grosch, “Fast and robust high dynamic range image generation with camera and object movement,” In Vision, Modeling and Visualization (VMV), pp. 277-284, 2006.
[4] A. O. Akyuz and E. Reinhard, “Noise reduction in high dynamic range imaging,”Journal of Visual Communication and Image Representation, archive Volume 18, Issue 5, pp. 366-376, May, 2007.
[5] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic tone reproduction for digital images,” Proc. SIGGRAPH, pp. 267-276, 2002.
[6] F. Drago, K. Myszkowski, T. Annen, and N. Chiba, “Adaptive logarithmic mapping for displaying high contrast scenes,” In Proceedings of Eurographics 2003, The Journal of Computer Graphics. Forum, Vol. 22, No. 3, pp. 419-426, 2003.
[7] A. Troccoli, S.B. Kang, and S. M. Seitz, “Multi-view multi-exposure stereo,” Proc.Symposium. on. 3D Data Processing, Visualization, and Transmission, pp. 861-868, June, 2006.
[8] J. Sun, N.N. Zheng, and H.Y. Shum, “Stereo matching using belief propagation,”IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 7, pp.787-800, 2003.
[9] P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient belief propagation for early vision,”Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,
Vol. 70, No. 1, pp. 261-268, 2004.
[10] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004.
[11] S. Birchfield and C. Tomasi, “Depth discontinuities by pixel-to-pixel stereo,” Proceedings of the Sixth IEEE International Conference on Computer Vision, pp. 1073-1080, January 1998.
[12] C. Harris and M. Stephens, “A combined corner and edge detector,” Proceedings of the 4th Alvey Vision Conference, pp. 147-151, 1988.
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